Systems and methods for web spike attribution

ABSTRACT

Systems and methods are disclosed that measure web activity bursts after ad broadcasts that may be sent to multiple persons. One system uses a cookie-less/cookie-optional, anonymous/personal-identification-not-required, method for web-based conversion tracking that will work on broadcast media systems such as television, and could also be applied to measuring spikes from email, radio, and other forms of advertising where an episodic ad event is broadcast to multiple parties, and where responses occur in a batch after the broadcast.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 62/002,654, entitled “Web Spike Attribution,”filed on May 23, 2014, and U.S. Provisional Patent Application No.62/032,947, entitled “Systems and Methods for Web Spike Attribution,”filed on Aug. 4, 2014, which are incorporated herein by reference intheir entireties.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally toweb-based conversion tracking on broadcast media systems.

BACKGROUND

Cookies, IP addresses, or other methods to track a person from theirinteraction with an ad to their conversion have been used. However,television effects are notoriously difficult to measure. Unlike onlineadvertising, there are no cookies to enable tracking of a user between atelevision advertisement view and an action. This has left televisionwith critical problems with the ability to measure and optimize airings,which results in a large number of irrelevant and poorly targeted ads.The present disclosure addresses this problem and presents experimentalfindings and solutions.

SUMMARY OF THE DISCLOSURE

According to certain embodiments, methods are disclosed for web spikeattribution. One method includes receiving, at a server, one or moreheterogeneous sources of media data, the media data including televisionviewing event; receiving, at the server, web activity data, the webactivity data including a time period during the television viewingevent and a time period prior to the television viewing event; andmeasuring, by the server, a delta web response due to the televisionviewing event based on the web activity data during the televisionviewing event and the web activity data at a time period prior to thetelevision viewing event.

According to certain embodiments, systems are disclosed for teaching atelevision targeting system to reach product buyers. One system includesa data storage device storing instructions; and a processor configuredto execute the instructions to perform a method including: receiving oneor more heterogeneous sources of media data, the media data includingtelevision viewing event; receiving web activity data, the web activitydata including a time period during the television viewing event and atime period prior to the television viewing event; and measuring a deltaweb response due to the television viewing event based on the webactivity data during the television viewing event and the web activitydata at a time period prior to the television viewing event.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims. As will beapparent from the embodiments below, an advantage to the disclosedsystems and methods is that multiple parties may fully utilize theirdata without allowing others to have direct access to raw data. Thedisclosed systems and methods discussed below may allow advertisers tounderstand users' online behaviors through the indirect use of raw dataand may maintain privacy of the users and the data.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1A depicts an exemplary analytics environment and an exemplarysystem infrastructure for modeling and detailed targeting of televisionmedia, according to exemplary embodiments of the present disclosure;

FIG. 1B depicts exemplary data feeds of one or more media agencies ofmedia plan data, according to exemplary embodiments of the presentdisclosure;

FIG. 1C depicts exemplary data feeds of one or more media agencies ofmedia verification data, according to exemplary embodiments of thepresent disclosure;

FIG. 1D depicts exemplary data feeds of one or more media agencies oftrafficking/distribution data, according to exemplary embodiments of thepresent disclosure;

FIG. 1E depicts exemplary data feeds of call center data of one or morecall centers, according to exemplary embodiments of the presentdisclosure;

FIG. 1F depicts exemplary data feeds of e-commerce data of one or moree-commerce data vendors, according to exemplary embodiments of thepresent disclosure;

FIG. 1G depicts exemplary data feeds of order data of one or more dataorder processing/fulfillment providers, according to exemplaryembodiments of the present disclosure;

FIG. 1H depicts exemplary data feeds of consumer data enrichment of oneor more audience data enrichment providers from one or more databureaus, according to exemplary embodiments of the present disclosure;

FIG. 1I depicts exemplary data feeds of guide data of one or more guideservices, according to exemplary embodiments of the present disclosure;

FIG. 1J depicts exemplary data feeds of panel data of one or more paneldata enrichment providers, according to exemplary embodiments of thepresent disclosure;

FIG. 2 depicts a graph of web activity in response to a TVadvertisement, according to exemplary embodiments of the presentdisclosure;

FIGS. 3A-3E depict web activity versus television advertisement airingsduring a national campaign, according to exemplary embodiments of thepresent disclosure;

FIG. 4 provides a graphical view of new visitors versus existingvisitors, according to exemplary embodiments of the present disclosure;

FIG. 5 depicts an example of a tratio-web spike landscape, according toexemplary embodiments of the present disclosure;

FIG. 6 depicts a web spike alignment report having a predetermined zoom,according to exemplary embodiments of the present disclosure;

FIG. 7 depicts a web spike alignment report having a predetermined zoom,according to exemplary embodiments of the present disclosure;

FIG. 8 depicts a web spike alignment report having a predetermined zoom,according to exemplary embodiments of the present disclosure;

FIG. 9 depicts a web spike alignment report having a predetermined zoom,according to exemplary embodiments of the present disclosure;

FIG. 10 depicts a web spike alignment report having a predetermined zoomand corresponding analysis, according to exemplary embodiments of thepresent disclosure;

FIG. 11 depicts a web spike alignment report having a predetermined zoomand corresponding analysis, according to exemplary embodiments of thepresent disclosure;

FIG. 12 depicts a web spike curve diagnostic report in graphical form,according to exemplary embodiments of the present disclosure;

FIG. 13 depicts a web spike curve diagnostic report in graphical form,according to exemplary embodiments of the present disclosure;

FIG. 14 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface,according to exemplary embodiments of the present disclosure;

FIG. 15 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface,according to exemplary embodiments of the present disclosure;

FIG. 16 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface,according to exemplary embodiments of the present disclosure;

FIG. 17 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface,according to exemplary embodiments of the present disclosure;

FIG. 18 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface,according to exemplary embodiments of the present disclosure;

FIG. 19 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface,according to exemplary embodiments of the present disclosure;

FIG. 20 depicts a graphical representation of steps involved ingenerating a web media performance report, according to exemplaryembodiments of the present disclosure;

FIG. 21 depicts a screenshot of a web spike media performance reportusing program as a dimension, according to exemplary embodiments of thepresent disclosure;

FIG. 22 depicts a screenshot of a web spike media performance reportusing daypart as a dimension, according to exemplary embodiments of thepresent disclosure;

FIG. 23 depicts a screenshot of a web spike media performance reportusing hour as a dimension, according to exemplary embodiments of thepresent disclosure;

FIG. 24 depicts a screenshot of a web spike media performance reportusing network as a dimension, according to exemplary embodiments of thepresent disclosure;

FIG. 25 depicts a screenshot of a web spike media performance reportusing program as a dimension, according to exemplary embodiments of thepresent disclosure;

FIG. 26 depicts a screenshot of a web spike media performance reportusing creative as a dimension, according to exemplary embodiments of thepresent disclosure;

FIG. 27 depicts an example of a web spike halo report, according toexemplary embodiments of the present disclosure;

FIG. 28 depicts an example of a web spike halo report, according toexemplary embodiments of the present disclosure;

FIG. 29 depicts an example of a web spike halo report, according toexemplary embodiments of the present disclosure;

FIG. 30 depicts an example of a web spike cluster response analysis,according to exemplary embodiments of the present disclosure;

FIG. 31A shows a web spike percentage lift for a time before through atime after a TV airing with spike magnitude expressed in terms ofpercentage lift over average baseline activity, according to exemplaryembodiments of the present disclosure;

FIG. 31B shows an experimental lift measurement measured usingdifference of differences calculated over a treatment and controlgeographic area for three web metrics, according to exemplaryembodiments of the present disclosure;

FIG. 31C shows a web spike percentage lift for a time before through atime after a TV airing with spike magnitude expressed in terms ofpercentage lift over average baseline activity, according to exemplaryembodiments of the present disclosure;

FIG. 31D shows an experimental lift measurement measured usingdifference of differences calculated over a treatment and controlgeographic area for three web metrics, according to exemplaryembodiments of the present disclosure;

FIG. 31E shows a web spike percentage lift for a time before through atime after a TV airing with spike magnitude expressed in terms ofpercentage lift over average baseline activity, according to exemplaryembodiments of the present disclosure;

FIG. 31F shows an experimental lift measurement measured usingdifference of differences calculated over a treatment and controlgeographic area for three web metrics, according to exemplaryembodiments of the present disclosure; and

FIG. 32 is a simplified functional block diagram of a computer that maybe configured as a device or server for executing the methods, accordingto exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The present disclosure relates to the measuring of web activity burstsafter an ad broadcast is presented to multiple people. The system of thepresent disclosure may use a cookie-less/cookie-optional,anonymous/personal-identification-not-required method for web-basedconversion tracking that may work on broadcast media systems, such astelevision. Additionally, such systems and methods may also be appliedto measuring spikes from email, radio, and other forms of advertisingwhere an episodic advertisement event is broadcast to multiple parties,and where responses occur in a batch after the broadcast.

The present disclosure uses a short latency effect to provide ameasurement for television campaigns. The present disclosure provides:(1) a description of algorithms that attribute web activity totelevision airings without the need for training and/or parametricassumptions; (2) a description of how the attributed web response can beused in a TV ad targeting system to automatically target TV ads to themost responsive media; (3) a demonstration of the above in a liveadvertiser television campaign; and (4) a quantified amount of TVactivity that was correctly identified using experimentation. All of theabove shows that web spike response may be workable as a conversiontracking signal for television.

As described in detail below, by aligning web activity with TVbroadcasts in time and space and applying some signal processingtechniques, it may be possible to measure web activity bursts that peakabout 13 seconds after traditional TV ad broadcasts. Using this effect,it may be possible to deploy a web-based TV conversion tracking systemthat will both work today on existing TV systems, and could be used forfuture IP-connected TV systems. Thus, enabling real-time optimization oftelevision ad targeting.

Various examples of the present disclosure will now be described. Thefollowing description provides specific details for a thoroughunderstanding and enabling description of these examples. One skilled inthe relevant art will understand, however, that the present disclosuremay be practiced without many of these details. Likewise, one skilled inthe relevant art will also understand that the present disclosure mayinclude many other related features not described in detail herein.Additionally, some understood structures or functions may not be shownor described in detail below, so as to avoid unnecessarily obscuring therelevant description.

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed DescriptionSection.

The systems and method of the present disclosure allow for the receivingand processing of TV (media) related data and consumer related data froma plurality of different data sources and of a variety of different datatypes and formats. Based on the received data, the systems and methodsmay build a model that may be used to estimate a probability of reachinga particular set of persons. The estimated probability may then be usedto determine a value associated with buying an advertisement spot withina television program for the advertisement.

System Architecture

Any suitable system infrastructure may be put into place to receivemedia related data to develop a model for targeted advertising fortelevision media. FIG. 1A and the following discussion provide a brief,general description of a suitable computing environment in which thepresent disclosure may be implemented. Although not required, aspects ofthe present disclosure are described in the context ofcomputer-executable instructions, such as routines executed by a dataprocessing device, e.g., a server computer, wireless device, and/orpersonal computer. Those skilled in the relevant art will appreciatethat aspects of the present disclosure can be practiced with othercommunications, data processing, or computer system configurations,including: Internet appliances, hand-held devices (including personaldigital assistants (“PDAs”)), wearable computers, all manner of cellularor mobile phones (including Voice over IP (“VoIP”) phones), dumbterminals, media players, gaming devices, multi-processor systems,microprocessor-based or programmable consumer electronics, set-topboxes, network PCs, mini-computers, mainframe computers, and the like.Indeed, the terms “computer,” “server,” and the like, are generally usedinterchangeably herein, and refer to any of the above devices andsystems, as well as any data processor.

Aspects of the present disclosure may be embodied in a special purposecomputer and/or data processor that is specifically programmed,configured, and/or constructed to perform one or more of thecomputer-executable instructions explained in detail herein. Whileaspects of the present disclosure, such as certain functions, aredescribed as being performed exclusively on a single device, the presentdisclosure may also be practiced in distributed environments wherefunctions or modules are shared among disparate processing devices,which are linked through a communications network, such as a Local AreaNetwork (“LAN”), Wide Area Network (“WAN”), and/or the Internet. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

Aspects of the present disclosure may be stored and/or distributed onnon-transitory computer-readable media, including magnetically oroptically readable computer discs, hard-wired or preprogrammed chips(e.g., EEPROM semiconductor chips), nanotechnology memory, biologicalmemory, or other data storage media. Alternatively, computer implementedinstructions, data structures, screen displays, and other data underaspects of the present disclosure may be distributed over the Internetand/or over other networks (including wireless networks), on apropagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave, etc.) over a period of time, and/or they may beprovided on any analog or digital network (packet switched, circuitswitched, or other scheme).

Use of the system of FIG. 1A may involve multiple initial steps ofsetting up data feeds that can be used to receive data for building oneor more models as described herein for evaluating television programs,estimating ad effectiveness, and estimating ad response.

One step may be to setup data feeds with one or more media agencies,which may ensure the collection of all the data about what media isbeing purchased, running, and trafficked to stations. This may alsoensure that there is an accurate representation of the availabletelevision media. This step may include setting up data feeds for one ormore of: media plan data (e.g., as shown in FIG. 1B), which may includedata that is produced by media buyers purchasing media to run in thefuture; media verification data (e.g., as shown in FIG. 1C), which mayinclude data that is generated by third-party verification services;and/or trafficking/distribution data (e.g., as shown in FIG. 1D), whichmay include sample trafficking instructions and/or order confirmationssent to TV stations; media response data which is the response ofviewers to the TV ad, captured either through web activity, phoneactivity or other responses; TV schedule guide data which comprises dataon upcoming program airings, TV set top box data which comprises arecord of viewing activity from set top box subscribers; TV panel datawhich comprises a record of viewing activity from television viewers.

Media plan data may include a station a commercial will run on, anadvertiser, topic information, a media cost associated with thepurchase, a phone number, and/or a web address that is associated withthe commercial for tracking purposes.

Third-party verification services may watermark commercials and monitorwhen the media was run across all TV stations. The data generated bythird-party verification services may be used to verify that a mediainstance that was purchased for an advertisement spot was actuallydisplayed on TV.

The sample trafficking instructions and/or order confirmation mayinclude a product that was purchased, and instructions that a station isto use when displaying a commercial.

Another step may be to setup data feeds with one or more call centers,which may ensure there is accurate data about callers that called intospecific phone numbers. This step may include receiving a call centerdata feed (e.g., as shown in FIG. 1E). Call center data may include anydata associated with phone responses to phone numbers displayed in acommercial.

Yet another step may be to setup one or more data e-commerce vendor datafeeds. E-commerce data feeds may be setup to receive recurring datafeeds with a vendor and/or internal system of an advertiser that recordsorders that come in from an advertiser's website (e.g., as shown in FIG.1F). E-commerce data may include orders that came in on an advertiser'swebsite, customer information, and/or a time, volume, and/or substanceof the orders. Another step may be to set up one or more web activityfeeds with a vendor and/or internal system of an advertiser that recordsweb activity corresponding to TV broadcasts.

Another step may be to setup one or more data orderprocessing/fulfillment data feeds. Data order processing/fulfillmentdata feeds may be setup to receive recurring data feeds with ordervendor and/or internal system that physically handles the logistics ofbilling and/or fulfillment. This step may ensure an accounting ofsubsequent purchases, such as subscriptions and for returns/bad debt,etc., and may ensure accurate accounting for revenue. This step may alsoinclude receiving data from a series of retail Point of Sale (“PoS”)systems (e.g., as shown in FIG. 1G). Order data may include a purchaserecord, subsequent purchases, debt collection information, and returninformation.

Another step may be to setup one or more audience data enrichment datafeeds with one or more data bureaus. This step may ensure that callers,web-converters, and/or ultimate purchasers have their data attributesappended to their record in terms of demographics, psychographics,behavior, etc. (e.g., as shown in FIG. 1H). Examples of data bureaus mayinclude Experian, Acxiom, Claritas, etc. This data may includeattributes about consumers from the various data bureaus, such asdemographics, psychographics, behavioral information, householdinformation, etc.

Yet another step may be to setup one or more data feeds with one or moreguide services. This step may ensure that forward looking guide servicedata is ingested into the system. This data may be programming based onwhat is going to run on television for the weeks ahead (e.g., as shownin FIG. 1I). This upcoming media may be scored to determine which ofthis media should be purchased. Program guide data may include datarelated to a future run of programming, such as a station, time, programname, program type, stars, and general text description.

Another step may be to setup one or more data feeds for panel dataenrichment. Data related to purchasers of products on television, settop box viewer records, and/or existing panels may be received as a datafeed and appended to an advertiser's purchaser data mentioned above(FIG. 1J). Panel data enrichment may include viewer/responder data, suchas demographic, psychographic, and/or behavioral data.

In another step, all of the underlying data may be put into production.For example, all of the data feeds setup from steps one through sevenmay be loaded into an intermediate format for cleansing, addingidentifiers, etc. Personally Identifiable Information (“PII”) may alsobe split and routed to a separate pipeline for secure storage. As shownin FIG. 1A, an analytics environment 100 may include a media processingsystem 102, an agency data system 104, an advertiser data system 106, anaudience data system 108, and a processed media consumer system 110.

At the next step, media plan data 104 a, verification data 104 b, and/ortrafficking data 104 c of the agency data system 104 may be received ata data feed repository 112 of the media processing system 102. Further,call center data 106 a, e-commerce data 106 b, and/or order managementdata 106 c of advertiser data system 106 may be received at the datafeed repository 112. Additionally, viewer panel data 108 a, guide data108 b, and/or consumer enrichment data 108 c of the audience data system108 may be received at the data feed repository 112. After one or moreof data feeds are received by the feed repository 112, data may beextracted from the data feeds by extractor 114 of media processingsystem 102.

At another step, business logic/models may be run for matching responsesand orders to media (“attribution”). In this step, the data extractedfrom the data feeds has been ingested into the system at the mostgranular form. Here, the phone responses may be matched up to media thatgenerated it. The e-commerce orders may be matched using statisticalmodels to the media that likely generated them. As shown in FIG. 1A,transformer 116, aggregator 118, and analytics engine 120 of the mediaprocessing system 102 may process the aggregated data of the data feeds.Analytics engine 120 may include various sub-engines, such as experimentengine 120 a, match engine 120 b, optimize engine 120 c, and/orattribute engine 120 d, to perform various analytical functions.

At yet another step, the analyzed data may be loaded into databases. Forexample, the data may have already been aggregated and/or finalvalidation of the results may have been completed. After this, the datamay be loaded by loader 122 into one or more databases 124 for use withany of the upstream media systems, such as data consumers system 110.These include the ability to support media planning through purchasesuggestions, revenue predictions, pricing suggestions, performanceresults, etc. One or more databases 124 may include customers database124, campaign database 124, station inventory database 124, performancedatabase 124, models database 124, and/or PII database 124.

At another step, the analyzed data may be used by presentation module126. In this step, all of the data may be accessible to the operators ofvarious roles in the media lifecycle. This may include graphical toolsfor media planning (where the targeting in this application primarilyfits), optimization, billing, trafficking, reporting, etc.

The above-described system may be used to gather, process, and analyzeTV related data. This data may then be used to identify certainavailable media instances, or advertisement spots, that an advertisermay purchase to display an advertisement. As will be described infurther detail below, advertisement spots, also referred to as mediainstances, may be evaluated and scored to assist an advertiser inchoosing which media instance to purchase.

Web Spike Alignment

As mentioned above, a short latency effect exists between televisionbroadcasts and web searches. For example, after a Super Bowl ad for amovie, there may be an increase amount of searches for the movie secondsafter the ad airs. These web spikes may be used as an attribution systemfor measuring the impact of any particular advertisement. Thesemeasurements may be used to optimize an ad targeting system.

Robust algorithms may attribute web activity to television airingswithout the need for training and/or parametric assumptions. This“model-less” may allow the system and method to work in a robust mannerand capture complex effects from the ads being tracked. Performanceanalyses may use the attributed web activity to identify the bestperforming ads. The attributed web response may then be used to create afeedback loop which will enable an ad targeting system to automaticallytarget ads to the most responsive media.

The present disclosure, as discussed in detail below, may perform thefollowing steps. In a first step, the method may align web activity withadvertising events in time and space. Then, the method may filter theweb activity to a subset. In an optional step, a parameter space may beautomatically searched to find the best combination of exclusion window,time grain, measure. Next, a measurement of a delta web responseobserved after each airing may be performed by subtracting backgroundactivity using one or more of various methods. This may effectivelymeasure a residual change in web activity that is different frombackground activity. Then, the delta web response may be attributed tothe airing. In an optional step, performance reports may be created fordifferent ads, including various parameters, such as creative, days,hours, networks, programs, and so on. Finally, in another optional step,the performance information may be fed back into a targeting system,which then optimizes its ad targeting.

In the following example, an advertiser may be running advertising andmay be maintaining a website. The advertiser may use the systems andmethods disclosed herein to measure an impact of the advertisementairings on their website.

l(m(t₁,z₁,G)) may be the impressions associated with a media airing attime t₁, time zone z₁ and geography G. w(t₂,z₂,G) may be a web trafficmetric, such as new visitors, at time t₂, time zone z₂, and geography G.The time zones may be represented as a number of hours to add fromGreenwich Mean Time (“GMT”). In this scenario, the web servers may belocated in a particular time zone, such as z₂.

The input to the system may be a set of media airings (M) and a set ofweb measurements (W), as represented by the following formulas:

M={m(t ₁ ,z ₁ ,G)};W={w(t ₂ ,z ₂ ,G)}

The system may then generate a set of attributed airings with the samecardinality or lower as the original media events, as shown by thefollowing formula:

AttributedAirings={(m,Δw,l,wpi)}

Where, Δw may be the web activity due to ad m, l may be the impressionsfrom the ad, and wpi may be Δw/l, which may be the web response perimpression due to the ad airing.

In order to align web activity and ad broadcasts, the media airings maybe mapped to the same time zone. The mapping may ensure that a mediaactivity and web activity both occurred at the same time. Then, the webactivity may be bucketed into the same geographic G and time buckets tfor both web traffic and television airings.

Local broadcasts have well-defined times and time zones. For example, alocal broadcast may be Abilene, Tex., which has a single time zone of“Central Time.”

National broadcasts may be recorded separately from local broadcasts.National media airings may span multiple times zones, and therefore, mayrequire different logic to operate. For example, the logic to operatemay differ based on whether the network is a live feed network or a dualfeed network.

Live feed networks may have a single video stream that runs at the sameuniversal time in all geographies. These types of airings may all berecorded using a designated time zone, such as “Eastern Time.”

Dual feed networks may re-broadcast east coast programming at the samelocal time for west coast programming. For example, dual feed networksmay air at 8 pm Eastern Time, and then the dual feed network may air aprogram again on at 8 pm Pacific Time, which is effectively 3 hoursshifted in universal time. Thus, two events may occur in a universaltime. For these broadcast, it may be needed to create a “Ghost Airing”that is a copy of the national airing, but with impressions scaled toWest Coast population and East Coast airing by its proportion ofpopulation.

From the above, several airings may effectively be occurring indifferent local geographies. These airings may be aggregated to create aspecial national geography. The special geography G=National may sumboth local and national broadcasts so that there is capturing of allbroadcast activity.

Upon obtaining the above discussed information, the media and webactivity may be aligned in time and analyzed for impact.

The sum of all web activity across all geographies may be defined asW(T, G), and the sum of all media activity across all geographies in thesame time-zone may be defined as l(M(T, G)), which may be calculatedwith the following formulas:

${W\left( {T,G} \right)} = {\sum\limits_{t \in T}{w\left( {t,z_{2},G} \right)}}$${I\left( {T,G} \right)} = {\sum\limits_{{t - {({{z2} - {z1}})}} \in T}{I\left( {M\left( {{t - \left( {z_{2} - z_{1}} \right)},z_{1},G} \right)} \right)}}$

After aligning the web activity with airing, it may be possible tocharacterize the shape of a web response curve. In one example, Lewisand Reiley (2013)'s Super Bowl Yahoo Search data was used, which showssearch queries for a brand-name every 10 seconds after a 30 second SuperBowl commercial aired for the same brand. A log-normal distribution wasused to fit the data (KS*=0.10; υ=5.2322; σ=1 0.2102; as shown in Table1 below). From a parameterized curve, as shown in FIG. 2, TV searchesmay occur rapidly after exposure to a TV ad. The peak response activityoccurs just 13 seconds after the end of the ad.

In comparison, after seeing a display ad for a retailer on a website,there is a peak for searching the same retailer's name at 23 secondsafter exposure. Therefore, TV ads seem to drive faster response relativeto display ads. These times may be influenced by an amount of content onweb pages hosting display ads, auto-completions on search engines, andother factors, and thus, numbers may not be definitive, but merelyprovide some guidance as to the approximate time-scale involved.

As shown above in FIG. 2, a Log-Normal fits to Yahoo searches receivedafter the start of a Super Bowl commercial with the dashed lines showing1 and 6 minute marks. Measurement regimes capture the peak activitywhich is within the first minute as this carries the highestsignal-to-noise ratio. After 6 minutes 50% of activity has been recordedand signal-to-noise begins to drop significantly.

Table 1, as shown below, provides a Log-Normal that fits to Super Bowldata including descriptive statistics. As shown below, aKolmogorov-Smirnov (“KS”) statistic may provide a good fit.

TABLE 1 Seconds between Seconds between start Display Ad and Search ofSuper Bowl Ad and Statistic Type on featured Brand Search on featuredBrand KS 0.07377 0.10356 Mode 21.62 s 43.27 s Mean 136.96 s 389.35 sStdev 213.21 s 710.06 s Sigma 1.1094 1.2102 Mu 4.3044 5.2322 Skew 8.443111.537 Kurtosis 246.78 562.26

Applying Filters to Improving Signal-to-Noise

Super Bowl search data may present a statistical argument that webeffects from T may be visible on very large broadcasts. It may be shownthat buying N small airings, each at 1/N the price, would have tocontend with the same degree of noise, but with 1/N the media effectstrength, and only a square root of N gain in t-test power. Thus, it maybe a net loss to execute more, smaller airings, even with the same mediabudget. However, Super Bowl airings may be economically infeasible formost advertisers. It may be possible that a $4 million dollar TV spotand a $400 spot have a similar level of signal detection through varioustechniques for increasing signal-to-noise ratios.

Several practical steps may be taken to improve the signal-to-noiseratio on small TV campaigns. Super Bowl ads cost $34.80 per thousandimpressions and/or CPM, where-as the national TV media CPM average wasonly $6.60 based in 2012. Therefore, small TV spots may buy about 5times more impressions for the same budget, which also increases signalamplitude.

One way to improve signal-to-noise ratio may be to use geographic areasG, if a TV advertisement is run in only a small number of geographicareas at higher media weight. Then, for a far less expensive campaign, ahigher weight may be applied per capita without incurring the cost of anational campaign.

Another way to improve signal-to-noise ratio may be to localize effectstemporally. About 13 seconds after the end of a commercial broadcast,signal-to-noise ratio is at its maximum. At that time, more visitors onthe site may be newly arrived due to the recent TV airing thanbackground. The time window may need to be fine enough to sample thishigh signal-to-noise region of a curve. For example, the differencebetween a 30 second sampling window and 1 day is a 1,800× reduction insignal mean. Therefore, localization in time with short time windows maybe critical for achieving a temporary signal-to-noise superiority.

Yet another way to improve signal-to-noise ratio noise may be toeliminate robotic activity. Bots tend to produce large volumes oftraffic and may completely mask human activity. Methods for eliminatingbot activity may vary. Bots may be designed to avoid detection, and onegood method may to use a system, such as Google Analytics, to captureand extract data, since this may be supported by Google's bot filtrationsystems.

Another way to improve signal-to-noise ratio noise may be to measure thetargeting of the television ads. “Targetedness” measures how well anadvertisement matches the audience. Untargeted ads may produce almost nolift at all. Targeted impressions, rather than simply viewers, may beused to estimate more reliable web spike results.

Another way to improve signal-to-noise ratio noise may be to filter tosubsets of traffic that have a higher prevalence of television behaviorthat is sought to be isolated. Real-time responses to a TV ad may occurfrom people watching the broadcast live, and tend to require a tablet ormobile device. Traffic may be more likely to visit the homepage, ratherthan a deep-linked page, and the activity may likely be from newvisitors who have not been on the site before. By focusing on this classof traffic more organic background activity may eliminated, which mayleave a higher signal-to-noise ratio for the TV generated traffic. Alist of the filters are below:

After aligning web activity with advertising events in time and space,the system and method of the present disclosure may apply one or morefilters. While the web effects from a TV advertisement, may be smalland/or require a very large broadcast, it may be possible to measure TVeffects even on small airings by accounting for a signal-to-noise ratio.Filtering the web activity may be down in through one or more processes,as described below.

One process may use geographic areas. For example, if a TV advertisementis run in geographic areas at a high media weight, then for a relativelylow spend, it may be possible to produce detectible effects. Anotherprocess may localize the effects temporally. The spike curve shape maybe exponential with a sharp peak followed by a rapid decline. Therefore,within 5 minutes of a broadcast, signal to noise ratio is greatest.Measurements within 10 minutes, 20 minutes, and 1 day later may havevery poor signal to noise ratios. Accordingly, localization in time maybe used to achieve a temporary signal-to-noise superiority.

Another process may be to apply signal filters to remove as muchbackground noise as possible. Immediate real-time responses to a TV admay occur from people watching the broadcast live, and usually with acomputing devices, such as a smart phone and/or a tablet. Traffic may bemore likely to visit the homepage, rather than a deep-linked page.Additionally, the activity may be from new visitors who have not been onthe website before. By focusing on this class of traffic, backgroundorganic activity may be eliminated, and thus, leaving a very high signalto noise ratio. A list of filters to be applied to the web activity ispresented below:

New Cookie Filter: Filtering for visitors who have been assigned acookie for the first time. This may eliminate traffic of non-web spikevisitors who have visited the site before, and thus, may increase themagnitude of the web spike compared to background activity

Homepage Requests Filter: Filtering for visitors who are requesting thehomepage, rather than a deep-linked page. As mentioned above, web-spikevisitors may be more likely to navigating to the homepage of the websitefor the first time in response to an advertisement. This filter may helpto eliminate repeat purchase traffic of non-web spike visitors.

NULL Referrer Requests Filter: Filtering for website requests with NULLReferrer, which are requests where the visitor is not known to havenavigated from a search engine, deep-link, or another method. The NULLReferrer Requests Filter may filter for web-spike visitors who havedirectly typed the URL into their web browser to access the site for thefirst time.

Mobile and Tablet User-Agents Filter: Filtering for requests in whichthe User-Agent is a mobile or tablet user agent string. Requests frommobile or table users may have a much greater response to televisionads.

Time Filter: Filtering for a web traffic's server clock time that wasmeasured within 5 minutes of the broadcast appropriately adjusted fortime zone. Web traffic's server clock time may also be aggregated within1 minute or less of a broadcast appropriately adjusted for time zone.

Geography Filter: Filter for a web traffic's IP originated from the samegeography as the TV broadcast.

Web Spike Alignment Example Embodiment

Presented below is a live TV advertisement that has been analyzed. Alive advertisement may be analyzed to determine if web spike responsesmay be detected on very small airings. The data is from a live TVcampaign that ran from Feb. 11, 2013 to Jul. 9, 2013 with 35,296airings. The airings were detected using digital watermarks. The averagespot cost was $143 per airing, which is less than Super Bowl airingscosts.

FIGS. 3A-3E depict web activity versus television advertisement airingsduring a national campaign. In this embodiment, aggregated data fromadvertiser web logs may be represent as 5 minute time buckets. FIG. 3Aprovides new visitor web activity using 5 minute time intervals andnational geography, from Jan. 20, 2013 to Feb. 25, 2013. The time seriesmay become more spikey around Feb. 11, 2013. The reason for the spikesmay be seen in close-up in FIG. 3B, which provides a zoomed in timescale. As shown in FIG. 3C, when revealing TV media, that the airingsthat are producing the spikes may line up with the web spikes. FIG. 3Ddepicts web spikes during around Feb. 18, 2013 national advertisementcampaign. FIG. 3E provides a targeted impression aligned with web spikesin a time series.

Web Spike Alignment Interactive User Interface

The web spike alignment may be displayed in an interactive userinterface (also referred to as a “web spike explorer”). The web spikealignment display may enable a user to drag forward and backward intime, to zoom, switch on or off airing impressions or targetedimpressions. When the web spike alignment display shows impressionsalong with web activity, the web spike alignment display may be able toshow a match between ad airings and web activity.

The web spike explorer may also be used to create a near-real-timeapplication. The web spike explorer may be combined with a broadcastmedia tracking services (e.g., Kantar BVS, MediaMonitors, etc.) and webtraffic analytics services/weblog processing systems (e.g., GoogleAnalytics, web log processing) to produce a display that is able to showairings, web activity, and provide updates close to real-time. The webspike explorer may also show upcoming airings and overlay predicted webactivity changes. One embodiment of web spike explorer may include acountdown clock to a next broadcast airing.

Attributing Web Activity to Advertising Events

The durability of web spikes may allow for these web effects to be usedto build a television conversion tracking system for TV effectivenessreporting and optimization. In order to build such a system, properties,as described above may be used. Specifically, observed web activity W(T,G) can divided into two parts: (a) the web activity due to thetelevision airing ƒ(I(M(T, G))), plus (b) background web activity thatwould have occurred anyway W_(NoTV)(T, G), as represented by the formula

W(T,G)=ƒ(M(I(T,G)))+W _(NoTV)(T,G)

which may be rearranged as:

ƒ(I(M(T,G)))=W(T,G)−W _(NoTV)(T,G)

When estimating the total effects of television, the exact parametricfunctional form of ƒ does not have to be identified. The exactparametric functional form of ƒ may be a complex function, require alarge number of factors, and involve complex interactions. Forattribution purposes, W_(NoTV)(T,G) (the background web activity thatwould have occurred without TV), may be estimated. Once estimated, forevery time-period and geography (T and G), an attributed televisionestimate using the formula above may be calculated. In other words, thebackground W_(NoTV)(T,G) may be estimated and removed from observedactivity to infer the activity due to TV.

Thus, the system may approach the attribution problem in as “model free”a fashion as possible and may learn what TV is not. In practice, thebackground traffic may tend to be more reliable to estimate thanactivity during TV airings, as there are many more observations of webactivity and it is often periodic and predictable. TV estimates thenbecome the observed residuals after subtraction of background, in thenarrow window during TV airings when there is high signal-to-noise. Fouralgorithms for removal of background activity are provided below, whichintroduce minimal assumptions.

Instantaneous Treatment Control

The first algorithm may be an instantaneous treatment-control algorithmthat works by estimating the web traffic without TV as being equal tothe web traffic in the time period before the airing. In the formulaprovided below, the web activity control period y indicates how manyperiods of time prior to the present may be used to create an averagefor web activity (e.g., y=1). Additionally, the algorithm requires thereto have been no media airings during z time-periods prior to the present(e.g., z=1), the exclusion window. This ensures that the previous webactivity is not being elevated by an earlier media event.

${W_{NoTV}\left( {T,G} \right)} = \left\{ \begin{matrix}{{\frac{1}{y}{\sum\limits_{t = 1}^{y}{{W\left( {{T - t},G} \right)}:{if}{\sum\limits_{t = 1}^{z}{I\left( {M\left( {{T - t},G} \right)} \right)}}}}} = 0} \\{{UNDEF}:{otherwise}}\end{matrix} \right.$

By using the web activity prior to the airing as the no TV baseline, thetreatment when TV was applied may be close in time to the control, andthe environmental conditions, including day-hour-minute traffic levels,of the website should be very similar in these two cases. Therefore, anychange in web visits may be likely to have been caused by the TV airing.

This algorithm has an advantage of being robust when faced withreal-world problems such as bots. Bots crawl various pages, and then maynot be seen again for some time. This may cause significant problemswhen estimating models of web activity, since global models, such asregression, may be severely disrupted by what appears to be largeamounts of web traffic.

However, because web activity immediately prior to the airing may beclose in time to time of airing, it may be likely that the bot will bepresent and generating a similar inflation during both time buckets Tand T−1. Therefore, if we have:

ƒ(I(T,G))=W(T,G)+BOT(T,G)−(W(T−1,G)+BOT(T−1,G))

If BOT(T−1, G)=BOT(T, G), then the estimate will not be affected. Thismay be likely to be true in many cases because bot activity iscorrelated in time.

Day-Hour-Minute Modeled Subtraction

The second algorithm may be day-hour-minute modeled subtraction, whichworks by building an expectation for the day-hour-minute bucket for webactivity on the website when TV is not present. One method may be totake an average for an day-of-week-bucket, hour-of-day-bucket, and aminute-bucket when a media airing has not occurred within the last ztime-periods prior to the day-hour observation.

${W_{NoTV}\left( {T,G} \right)} = {{{E\left\lbrack {W\left( {Y,G} \right)} \right\rbrack}:{\sum\limits_{t = 1}^{z}{I\left( {M\left( {{T - t},G} \right)} \right)}}} = 0}$⋀DD(Y) = DD(T)⋀HH(Y) = HH(T)⋀MM(Y) = MM(T)W_(NoTV)(T, G) = UNDEF : otherwise

Where DD(Y) is the day-of-week for time bucket Y, HH(Y) is the hour ofday of time-bucket Y, MM(Y) is the minute bucket for Y, and E[ ] is themean. For 7 days, 24 hours and 0 minute buckets, this may be equivalentto defining 168 day-hour 1-0 dummy variables in a regression model thatis trying to predict the non-TV background web activity.

The day-hour-minute subtraction algorithm may be a global model. Thealgorithm may be susceptible to problems including: (a) in-time trendsin web activity (for example, if a website's traffic is growing, thenthe method may incorrectly begin to estimate more activity due totelevision); (b) bot activity, which may cause outliers to pull thenumbers out; and (c) transient background changes due to interferencefrom other TV airings from the same campaign. Nevertheless, forwell-spaced airings the algorithm may be effective.

Instantaneous With Dynamic Pre-Periods

A third algorithm may be “dynamic pre-periods,” which rather thanexcluding an airing if there is activity in the last z time buckets,instead, attempts to assemble a baseline using as many periods of datathat are possible, up to the next airings in the past, or z, whicheveris later. Therefore, if there was an airing M₁ a 45 minutes in the pastand another M₂ 15 minutes in the past, and the maximum time window z is30, then this algorithm may take the time period 14 minutes in the pastuntil 1 minute in the past as the baseline, as shown in the followingformula:

${W_{NoTV}\left( {T,G} \right)} = {\frac{1}{y}{\sum\limits_{t = 1}^{y}{W\left( {{T - t},G} \right)}}}$

Where y=max(PrevAiring(M(T, G)), z), and PrevAiring is the most recentairing preceding M.

The dynamic pre-period algorithm may be effective in attributing alarger number of airings than the instantaneous treatment controlalgorithm with exclusion periods. However, the variable baseline periodmay introduce some endogeneity because an airing at 5 minutes ago, 10minutes ago, and 30 minutes ago may each retain a different degree ofresidual lift. Thus, a baseline level when the most recent airing is 5minutes ago may be higher, than when the most recent airing is 30minutes ago. Since the baseline may be contaminated with a differentdegree of historical lift on each airing, error may be introduced inmeasuring the effect of television. The uniform exclusion period methodmay be able to minimize baseline lift by selecting a large exclusionperiod, and also not having a changing amount of baseline lift.

Handling Simultaneous Airings

When two or more airings simultaneously occur within the same timeperiod, two potential airings may have caused the spike. Thus, aquestion may be which airing should take credit. There are two methodsof handling this case: (a) set the airing attribution to missing becauseattribution is unclear, or (b) attempt to apportion credit.

Although the above-described system is preferred to be as “model-free”as possible, a useful heuristic for partial attribution is to applycredit in proportion to each airing's television impressions as apercentage of total.

Thus, let S={M_(i)} be the set of airings which are regarded as airingsimultaneously and ƒ(S) be the estimated TV effect for the group ofairings. If the full attribution method is used we set the estimate toundefined, as follows:

ƒ(M _(i))=UNDEF

Using the this simultaneous airings method, individual airing TV effectsmay be estimated as follows:

${f\left( M_{i} \right)} = {\frac{I\left( M_{i} \right)}{\sum_{i}{I\left( M_{i} \right)}}{f(S)}}$

For example, a total web traffic estimated as due to television may bef=100. Also, assume there were three airings with 100, 700, and 200impressions, respectively, each occurred at exactly the same time. Thus,the middle airing would receive 70% of the credit.

The simultaneous airings method may prove to be useful in practice, whenlarger time buckets are need to be used due to web tracking constraints.For example, when 15 minute buckets are used, many more times airingsmay “collide” with other airings in the same bucket.

However, this method may be problematic if the airings vary widely intargeting quality. For example, the first airing may reach the idealtarget, whereas the second might be running in the middle of the earlyhours of the morning when none of the target are watching. As a result,the method may introduce some error to the attribution results. However,the benefit of this method is that it enables more airings to beattributed and minimizes the number of factors introduced.

Alternatives may be available. For example, targeted impressions may beused for credit assignment above, such as timp(M_(i)) instead ofI(M_(i)). In this example, assignments may tend to be more accurate.However, in creating a targeting landscape (discussed in more detailbelow) and in order to target effectively, an estimated web attributionwithout knowledge of targeting may be used. Additionally, it is alsopossible to use cost to allocate credit, but accuracy may decrease.

Combined Algorithm

In one embodiment, a combine algorithm may be used. For example, theday-hour-minute subtraction and instantaneous treatment controlalgorithm may be combined. Further, other algorithms may be combinedwith one or both of the day-hour-minute subtraction and instantaneoustreatment control algorithm.

Web Spike Algorithm Call

An example call to attribute a web spike is below:

exec Web.Attribution

110575, —sourcekey/advertiser

98, —jobid; batch of airings being attributed

NULL, —measureid; if NULL then do all measures

0, webspikecurvebymarket, —1==do by market, 0==aggregate tomarketmaster=169

1, —webtimewindow, 1==1 minute

30, —lowermediaexclusiontimewindow minutes

30, —uppermediaexclusiontimewindow minutes

60, —webspikelowerwindow minutes

60, —webspikeupperwindow minutes

1, —fullattribution=1 means attribute all using the simultaneous airingsalgorithm. 0 means only attribute singletons

1001,—minimpressions; do not show results for airings with <1001impressions

1,—1−minairings; do not show results for airings<1

‘2015-01-01’, —baseline period start for the month-day-hour algorithm

‘2015-02-01’, —baseline period end for the month-day-hour algorithm

30,

60,

1—offset in minutes;

The above may apply a small adjustment to a t0 time of the airing. Mostairings may be listed with their respective start times. When workingwith 1 minute or lower time buckets, sampling a time of the airing maybe too early to sample a peak web response. Most traffic may reach awebsite within 13 seconds after the end of the TV airing. This may meanthat the 43rd second is the peak activity. Thus, instead of sampling the0 second, sample the 1 minute. For lower time buckets, this adjustmentmay be needed to the airing start time to ensure that the peak may besampled.

Diagnostic: Spike Percentage

One diagnostic measure that may help calculate an effect size of a webspike is to measure a percentage change in web activity compared to abaseline at a range of times before and after an ad airing. Once allmedia airings that meet the various parameters described above have beenfound, the diagnostic measure may be calculated as follows:

-   -   (Time-before-webspike, MeasureID, Delta web response,        average-measure-value, PercentChange)

Where Time-before-webspike may be a time in seconds prior to TV airings,MeasureID may be a web measure (e.g. visitors, homepage new visitors),Delta Web Response may be an average change in web traffic, andAverageMeasureValue may be an average for the measure.

Table 2, as shown below, provides a variety of traffic classes and theimpact of television on that traffic. Delta over base may be equal tothe highest reading divided by the mean reading. PercentChange (shownbelow) or Percent increase over base may be the measure's value dividedby base activity, where base activity is equal to the preceding readingin the y minutes prior to the airing, and where no other airing occurswithin those y minutes. In Table 2, additional traffic during a 5 minuteairing is web traffic in native units that was generated during the TVairing. Percentage increase over a base is the during-airing readingdivided by base activity. Base activity is equal to the reading in the30 minutes prior to the airing, and where no other airing occurs withinthose 30 minutes. tImp Correlation is the correlation coefficientbetween the metric and TV airing's targeted impressions, which mayproduce a more accurate measure since the other metrics do not accountfor targeting. Increase per million impressions (“wpmm”) is the changein the web traffic multiplied by one million divided by number oftelevision impressions. Mean and Var are the mean and variance of theweb traffic in native units. A p-value is shown based on a t-test forthe instantaneous spike percentage change, during the time of airing,compared to the percentage changes of differences around baselineactivity. % of events shows the web traffic as a percentage of all webtraffic. Some web traffic categories may have high spike response frominfrequent events.

${PercentChange} = {100 \cdot \frac{{\sum{W\left( {T,G} \right)}} - {W_{NoTV}\left( {T,G} \right)}}{\sum{W_{NoTV}\left( {T,G} \right)}}}$MeanAdditionalTraffic=PercentChange*MeanUnits

A p-value is shown based on comparison of the activity during the spikecompared to a normal distribution of differences around baselineactivity.

In order to measure web spikes quantitatively, several measurements forweb spike magnitude are provided in Table 2 below. Web metrics that aremore filtered may be able to better identify the impact of thetelevision airing.

TABLE 2 Delta Percent per base Change Mean unit (% 5 addtl. per millmin. inc. traffic (Inc per over during 5 mill. base w min of Mean Var %of tlmp lmps 30 min. TV Metric Units Units events Corr. wpmm) excl.)airing New home mobile 5.1 34  2% 0.069 9.64 75.6% 3.9 New home direct34.6 446 11% 0.057 2.45 34.4% 11.9 Mobile 23.4 188  7% 0.055 2.95 34.1%8.0 New home 58.7 1,035 18% 0.055 2.14 30.7% 18.0 Home Page 58.7 1,03518% 0.055 2.14 30.7% 18.0 Direct To Site 53.2 617 17% 0.052 1.65 25.3%13.5 No Referrer 53.2 617 17% 0.052 1.65 25.3% 13.5 Tablet 14.9 101  5%0.035 1.64 24.0% 3.6 New Visitors 164.3 5,889 52% 0.035 0.89 14.8% 24.3Unique Visitors 262.2 15,565 83% 0.033 0.62 10.9% 28.6 Visits 291.619,930 92% 0.028 0.59 10.2% 29.7 SEM 54.6 866 17% 0.023 0.67 9.1% 5.0Desktop 126.0 3,663 40% 0.027 0.46 7.8% 9.8 SEO 36.8 458 12% 0.016 0.467.3% 2.7 Existing Visitors 97.9 2,569 31% 0.028 0.16 2.8%* 2.7 CategoryPage 60.6 980 19% 0.009 0.25 2.3%* 1.7 Email 4.8 61  1% 0.017 0.36 1.7%(ns) 0.1 Product Page 33.1 310 10% 0.024 0.07 0.7% (ns) 0.2 Affiliates7.3 20  2% 0.028 0.19 0.0% (ns) 0 Display 0.7 1  0% 0.008 0.23 na 0*Significant p < 0.05; (ns) Not significant; All other metrics aresignificant at p < 0.01

FIG. 4 provides a graphical view of new visitors (upper line) versusexisting visitors (lower line). The existing visitor time series isalmost un-moved when there are TV airings with only a 2.8% spike whichis not statistically significant (Table 2). In contrast, new cookievisitors shows distinct spikes that match TV airings that are 14.8% inmagnitude and are significant. As shown in FIG. 3, web spikes may tendnot to show up in Existing Visitor traffic (lower line).

Referring back to Table 2, the highest signal-to-noise ratio for anytraffic category is “New Cookie Visitor traffic with Mobile User-agentsthat reach the Homepage.” The web spike here is a dramatic 76% higherthan the baseline activity (p<0.01; see Table 2 and FIG. 3). This liftoccurs on 2% of the site's traffic.

Most categories of traffic show TV spikes that are statisticallysignificant. However, three categories appear to have minimal changefrom TV. Only 13% of the traffic: “Email,” “Product Page,” and“Affiliate traffic” are largely not impacted by television (spikemagnitude<=1.7%; not statistically significant). Product pages may haveminimal change since these are “deep-linked pages,” which would likelybe reached after a search for a specific product, where-as the TV airingtends to produce branded searches and first time visitor activity to thehome page. Email may have minimal change given that email campaignsoccur on an episodic basis and may be likely to be uncorrelated withtelevision advertising. Thus, the least responsive traffic to TV may bedeep-linked page browsers and email responders.

Significant amounts of paid and organic search traffic may bemis-attributed. A considerable amount of paid search and organic searchtraffic at the time of a TV airing may be actually due to the televisionbroadcast (see Table 2; FIG. 3). A 9.1% spike was measured on PaidSearch (SEM) (p<0.01) and 7.3% on Organic Search (SEO) (p<0.01), withboth increases statistically significant. These effects may be importantbecause these are large sources of traffic, and so these lifts maytranslate into a large increase in traffic that can be measured as dueto television.

The ability to measure effects on these digital marketing channels maybe important because most conversion tracking systems on paid search maymis-attribute this traffic. A problem with current last-clickattribution systems is that the search query or paid link receives 100%credit for the conversion, and often results in brand name keywords(e.g., for a company called “Physicians Mutual Insurance” a brand namekeyword would be “Physicians Mutual”) with thousands of conversions andcost per acquisitions of pennies. It may appear that these keywords arethe most effective advertising vehicles for producing conversions, whenin fact the users typing in these brand name keywords may already knowof the company and are trying to navigate to a website of the company.The web spike analysis reported here may confirm that a lot of searchclick-throughs that are being credited to keywords, are in factoccurring due to untracked television broadcasts.

Diagnostic: Targeting Landscape and Slope Calculation

An automated targeting landscape may be generated based on theattributed web response to the set of airings. This analysis may rely ona score being assigned to each ad airing, which is referred to as atargeting score and/or tratio(M_(i)). The targeting score and/or tratiomay measure how well targeted an ad is.

The ad airings with their targeting scores, the resulting, attributedweb responses, and the impressions associated with each ad airing may becollected. Then, the targeting scores may be segmented, such as bypercentiles. For each targeting score bucket, the sum of web deltaresponse may be calculated and divided by the sum of impressions. Thisinformation provides such parameters as tratio_ntile, measure,delta-web-response, impressions, and delta-web-response-per-impressionor WPI.

In a first step, the airings with their targeting scores are collected,and all of the resulting, attributed web responses, and the impressionsassociated with each airing are collected. The data collected mayinclude (Mi, measureID, tratio(M_(i)), delta-web-response, impressions)

Next percentile/decile or n-tile may be calculated for the targetingscores in order to ensure the targeting scores are grouped together inton bins. For each targeting score bin, the sum of web delta response iscalculated and divide by the sum of impressions, which produces thefollowing data: (tratio_ntile, measure, sum_of_delta-web-response,impressions, sum_of_delta-web-response-per-impression).

Finally, the relationship between targeting score tratio_ntile anddelta-web-response-per-impression or WPI is estimated. This may becalculated as follows which is a weighted least squares fit mappingtratio to expected wpi, weighted by impressions.

Let x=tratio, y=WPM (web response per million impressions), andw=impressions/sum(impressions). The slope and intercept may becalculated as follows:

${\overset{\_}{x} = {\sum\limits_{i}{w_{i}x_{i}}}};{\overset{\_}{y} = {\sum\limits_{i}{w_{i}x_{i}}}};$$\beta_{1} = \frac{\sum_{i}{{w_{i}\left( {x_{i} - \overset{\_}{x}} \right)}\left( {y - \overset{\_}{y}} \right)}}{\sum_{i}{w_{i}\left( {x_{i} - \overset{\_}{x}} \right)}^{2}}$$\beta_{0} = {\overset{\_}{y} - {\beta_{1}\overset{\_}{x}}}$

β₁ may be the slope of the linear relationship between tratio and webspike response per million. β₀ may be the intercept for the same.

Root mean squared error may then calculated for the above landscape inorder to provide a measure for the quality of the resulting landscape.This may be calculated as:

RMSE=Σ√{square root over (w _(i)(β₁ x+β ₀−WPI)²)}

FIG. 5 depicts an example of a tratio-web spike landscape. As shown inFIG. 5, tratio is plotted on the x-axis against a web response permillion impressions (WPM) on the y-axis. Tratio, as depicted, may be ameasure of how targeted teach impression is. A circle size may representthe number of impressions that were tested at a given tratio level.Using the analysis of FIG. 5, an advertiser may take any airing, alongwith a targeting score, and may estimate the web response per millionthat an airing may generate if the advertiser buys it. A user mayestablish a desired cost per web visitor (cost per delta goal), andthen, with knowledge of the impressions and tratio of the airing, mayestimate a cost needed to negotiate for the airing that may achieve thegoal. For example, cost=cost per deltagoal*WPM(tratio(airing))/1,000,000.

Reports

A variety of reports may be generated showing the web spike results.Such reports may include a web alignment report, a web spike curvereport, a web spike attribution report, a web media performance report,a web spike halo report, and/or a web spike cluster responsivenessreport.

Web Spike Alignment Report

A web alignment report may show web activity and media airings thatallow a user to visualize what media is running and its impact. Further,a web alignment report may show the impressions or targeted impressionsassociated with an ad airing. For example, as impressions increase, aweb response may be expected to increase. The web alignment report mayinclude the one or more of the following parameters datetimestamp,geography, measureid, delta web response, impressions, timpressions,airings, tratio, delta web response per impression,programname_largestairing, and/or network_largestairing.

FIG. 6 depicts a web spike alignment report having a predetermined zoom.As shown in FIG. 6, a web spike alignment report having a zoom of 1 mayshow an advertiser's web traffic and television airings. The largerspikes in web activity may be caused by television commercials. Theboxed area is a region of interest that may be further zoomed in on.

FIG. 7 depicts a web spike alignment report having a predetermined zoom.As shown in FIG. 7, a web spike alignment report having a zoom of 2 mayreveal that TV airings may match spikes in web activity. Televisionprograms associated with the TV airings may be superimposed on the graphfor each of interpretation. The boxed area is a region of interest thatmay be further zoomed in on.

FIG. 8 depicts a web spike alignment report having a predetermined zoom.As shown in FIG. 8, a web spike alignment report having a zoom of 3 mayshow sharp increases and exponential declines associated with webspikes. The boxed area is a region of interest that may be furtherzoomed in on, which is, for example, TLC's show “I didn't know I waspregnant.”

FIG. 9 depicts a web spike alignment report having a predetermined zoom.As shown in FIG. 9, a web spike alignment report having a zoom of 4 mayshow a close-up view of a television show, such as TLC's show “I didn'tknow I was pregnant.” The report may show the sharp increases associatedwith the time of the airing, and then show a decline.

FIG. 10 depicts a web spike alignment report having a predetermined zoomand corresponding analysis. As shown in FIG. 10, a web spike alignmentreport having a zoom of 4 may have a baseline period of average webactivity leading up to a web spike peak of web activity. Then, after theweb spike peak there is a decline and a post web spike backgroundperiod.

FIG. 11 depicts a web spike alignment report having a predetermined zoomand corresponding analysis. As shown in FIG. 11, a web spike alignmentreport may have exclusion periods that provide a way of filtering outairings that may have an ambiguous and/or misleading signal. If thereare airings that are too close together in time, it may not be possibleto obtain a “clan” measure of the pre-airing web activity baseline. As aresult, an exclusion period may be used, such as 30 minutes prior to anairing. Thus, if the exclusion period is implemented, attributingquestionable web spikes may be avoided.

Web Spike Curve Diagnostic Report

A web spike curve diagnostic report may be an analysis report that showsthe percentage change in web activity when an ad airing is detected. Theweb spike curve diagnostic report may not take into account the size ofthe ad airing in terms of number of impressions, and may be an imprecisemeasure. However, the web spike curve report does show on average thepercentage change in web activity when there is an ad running. The webspike curve report may provide a rough, but easy to understand measureof how effective the ads are at driving traffic. The web spike curvereport may include one or more of the following parameterstime-to-spike, geography, measureid, delta web response, impressions,timpressions, airings, tratio, and/or delta web response per impression.

Table 3, as shown below, depicts an example of a web spike curve report.The web spike curve report shows that the minutes away from an ad airingversus activity in percentage higher than baseline.

TABLE 3 Minutes New after New Exising home New DirectTo No Home CategoryProduct airing visitors visitors mobile home SEM SEO Affiliate SiteReferrer Page Page Page −60 −5%  −6%  7% −2%  −4%  −10%  1% 1% 1% −2% −6%  −4% −55 −4%  −5%  7% 0% −4%  −5%  1% −1%  −1%  0% −5%  −7% −50 −2% −4%  0% −1%  −2%  −5%  1% 0% 0% −1%  −3%  −1% −45 −1%  −4%  0% 0% 0%−5%  1% 2% 2% 0% −2%  −1% −40 0% −1%  0% 0% 0% −2%  1% −1%  −1%  0% −2% −1% −35 0% −1%  0% 1% 0% −2%  1% 0% 0% 1% −2%  −1% −30 −1%  0% 0% 0% 0%1% 1% 0% 0% 0% 0% −1% −25 1% 1% 0% −1%  1% 1% 1% −4%  −4%  −1%  0%  2%−20 0% 0% 0% −1%  0% 1% 1% −1%  −1%  −1%  2% −1% −15 0% 2% −9%  −1%  1%1% 1% −1%  −1%  −1%  0% −1% −10 2% 2% 0% 0% 1% 1% 1% 0% 0% 0% 3%  6% −53% 3% 0% 1% 2% 3% 1% 2% 2% 1% 3%  2% 0 24%  17%  91%  43%  23%  25% 43%  38%  38%  43%  19%  25% 5 4% 4% 0% 5% 3% 3% 1% 3% 3% 5% 5%  2% 102% 4% 0% 3% 3% 3% 1% 2% 2% 3% 3%  2% 15 3% 3% 0% 1% 3% 3% −14%  2% 2% 1%3%  2% 20 2% 3% 0% 0% 3% 3% 1% −1%  −1%  0% 2%  2% 25 2% 1% 0% 1% 1% 3%1% 0% 0% 1% 2%  2% 30 2% 0% 0% 1% 2% 3% 1% 2% 2% 1% 2%  2% 35 0% 0% 0%−2%  −1%  1% 1% −1%  −1%  −2%  2% −1% 40 −1%  −1%  0% −2%  −1%  1% 1% 0%0% −2%  0% −1% 45 −1%  0% 0% 1% −1%  1% 1% 2% 2% 1% 0% −4% 50 −1%  −1% 0% −2%  −3%  1% 1% −1%  −1%  −2%  0% −1% 55 −2%  −3%  0% −2%  −1%  −2% 1% −1%  −1%  −2%  −2%   2% 60 −3%  −3%  0% −3%  −3%  −2%  1% −1%  −1% −3%  −3%  −4%

FIG. 12 depicts a web spike curve diagnostic report in graphical form.As shown in FIG. 12, a percentage of web traffic changes when atelevision ad airs is shown. The x-axis may show the minutes before,during, and after the television ad. The y-axis may show a percentage,which is an average over all time periods (−60 minutes to +60 minutes)around a television airings. The bars on the graph represent a standarddeviation.

FIG. 13 depicts a web spike curve diagnostic report in graphical form.As shown in FIG. 13, a percentage of web traffic changes when atelevision ad airs is shown. The x-axis may show the minutes before,during, and after the television ad. The y-axis may show a percentage,which is an average over all time periods (−60 minutes to +60 minutes)around a television airings.

Web Spike Airing Attribution Report

A web spike airing attribution report may provide information on thedelta web response for every ad airing, which may be a main output fromattribution. After the web spike airing attribution report is generated,other reports may be derived from this report. A web spike attributionreport may include one or more of the following parameterspanelairingid, datetimestamp, geography, measureid, delta web response,impressions, timpressions, tratio, delta web response per impression,programname, and/or network.

Table 4, as shown below, provides a web spike airing attribution reportwith the parameter specific airings with delta web response predictionsadded. The parameter WebAttributionID joins to theWebAttributionSettings, which specifies the parameters for a particularweb attribution. MarketMasterid of 169 may refer to a nationalgeography. Sourcekey of 110497 may refer to the advertiser's campaign.LastJobID may refer to a JobID for a set of airings that were trackedfor the advertiser. Slope and intercept may be calculated parameters fora tratio-WPM landscape that estimates the WPM for different tratio.SpikePCTAvg may be an average change in web activity when a TV airingruns

TABLE 4 Broadcast Airing 1 2 3 4 sourceairingid 263786 263787 270014262065 WebAttributionID 1174 1174 1174 1174 MarketMasterID 169 169 169169 PanelAiringID 148065 148066 149421 148053 AirDate Jun. 10, 2013 Jun.10, 2013 Jun. 10, 2013 Jun. 10, 2013 1:15 AM 1:45 AM 3:00 AM 4:45 AMAirDatePST Jun. 9, 2013 Jun. 9, 2013 Jun. 10, 2013 Jun. 10, 2013 10:15PM 10:45 PM 12:00 AM 1:45 AM AirDateEST Jun. 10, 2013 Jun. 10, 2013 Jun.10, 2013 Jun. 10, 2013 1:15 AM 1:45 AM 3:00 AM 4:45 AM Value 22 21 13 11ValueStdev 0 0 0 0 Delta 4.75 8 5.5 4.5 DeltaStdev 3.3040379342.708012802 2.081665999 3.31662479 DeltaPositive 4.75 8 5.5 6DeltaPositiveStdev 3.304037934 2.708012802 2.081665999 1.732050808 WPI−2.88281E−05 −4.85525E−05 −0.000180174 −0.006 Impressions 164770 16477030526 1000 TotalAirings 1 1 1 1 DeltaStarting NULL NULL NULL NULLDeltaPositiveStarting NULL NULL NULL NULL DeltaPositivePct NULL NULLNULL NULL DeltaPct NULL NULL NULL NULL DeltaPctStdev 0.1501835420.128952991 0.160128154 0.301511345 DeltaPositivePctStdev 0.1501835420.128952991 0.160128154 0.157459164 TRatio 0.07308273 0.073082730.31722299 0.40527069 MatchFailure 1 1 NULL 3 PriorAiringMinutes 300 3075 105 NextAiringMinutes 30 75 105 30 WebAttributionID 1174 1174 11741174 WebAttributionAiringID 11 11 11 11 CreateDate May 13, 2014 May 13,2014 May 13, 2014 May 13, 2014 3:17 PM 3:17 PM 3:17 PM 3:17 PMLastRunDate May 13, 2014 May 13, 2014 May 13, 2014 May 13, 2014 3:17 PM3:17 PM 3:17 PM 3:17 PM UseForScoring 1 1 1 1 SourceKey 110497 110497110497 110497 LastJobID 13 13 13 13 MeasureID 1 1 1 1 AggregateByMarket0 0 0 0 DataTimeWindow 15 15 15 15 LowerMediaExclusionTimeWindow 60 6060 60 UpperMediaExclusionTimeWindow 60 60 60 60 WebSpikeCurveLowerWindow60 60 60 60 WebSpikeCurveUpperWindow 60 60 60 60 FullAttribution 1 1 1 1MinimumImpressions 0 0 0 0 MinimumAirings 1 1 1 1 SpikePctAvg 3.91 3.913.91 3.91 Slope 14.32062195 14.32062195 14.32062195 14.32062195Intercept 1.472022791 1.472022791 1.472022791 1.472022791 SlopePositive8.600751794 8.600751794 8.600751794 8.600751794 InterceptPositive20.30765463 20.30765463 20.30765463 20.30765463 AttributedPct 100 100100 100 Broadcast Airing 5 6 7 sourceairingid 262075 270023 262385WebAttributionID 1174 1174 1174 MarketMasterID 169 169 169 PanelAiringID148054 149430 148055 AirDate Jun. 10, 2013 Jun. 10, 2013 Jun. 10, 20135:15 AM 5:45 AM 10:30 AM AirDatePST Jun. 10, 2013 Jun. 10, 2013 Jun. 10,2013 2:15 AM 2:45 AM 7:30 AM AirDateEST Jun. 10, 2013 Jun. 10, 2013 Jun.10, 2013 5:15 AM 5:45 AM 10:30 AM Value 4 7 30 ValueStdev 0 0 0 Delta−1.75 −1.5 −10.5 DeltaStdev 2.753785274 3.785938897 16.94107435DeltaPositive 1 4 4 DeltaPositiveStdev NULL NULL 4.242640687 WPI0.002333333 8.87942E−05 0.000312082 Impressions 1000 16893 33645TotalAirings 1 1 1 DeltaStarting NULL NULL NULL DeltaPositiveStartingNULL NULL NULL DeltaPositivePct NULL NULL NULL DeltaPct NULL NULL NULLDeltaPctStdev 0.688446318 0.540848414 0.564702478 DeltaPositivePctStdevNULL NULL 0.141421356 TRatio 0.40527069 0.31174507 0.38243402MatchFailure 3 NULL NULL PriorAiringMinutes 30 30 285 NextAiringMinutes30 285 30 WebAttributionID 1174 1174 1174 WebAttributionAiringID 11 1111 CreateDate May 13, 2014 May 13, 2014 May 13, 2014 3:17 PM 3:17 PM3:17 PM LastRunDate May 13, 2014 May 13, 2014 May 13, 2014 3:17 PM 3:17PM 3:17 PM UseForScoring 1 1 1 SourceKey 110497 110497 110497 LastJobID13 13 13 MeasureID 1 1 1 AggregateByMarket 0 0 0 DataTimeWindow 15 15 15LowerMediaExclusionTimeWindow 60 60 60 UpperMediaExclusionTimeWindow 6060 60 WebSpikeCurveLowerWindow 60 60 60 WebSpikeCurveUpperWindow 60 6060 FullAttribution 1 1 1 MinimumImpressions 0 0 0 MinimumAirings 1 1 1SpikePctAvg 3.91 3.91 3.91 Slope 14.32062195 14.32062195 14.32062195Intercept 1.472022791 1.472022791 1.472022791 SlopePositive 8.6007517948.600751794 8.600751794 InterceptPositive 20.30765463 20.3076546320.30765463 AttributedPct 100 100 100

Table 4 depicts an example of a web spike attribution report withairings running as columns and attributes including the attributed webspike delta running down the page.

FIG. 14 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface.As shown in FIG. 14, a user may select a “web attribution airing”report.

FIG. 15 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface.As shown in FIG. 15, a user may select an advertiser for a webattribution airing report.

FIG. 16 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface.As shown in FIG. 16, a user may select an attribute measure/parametersfor the web attribution airing report.

FIG. 17 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface.As shown in FIG. 17, a user may select a start date and an end date forthe web attribution airing report.

FIG. 18 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface.As shown in FIG. 18, a user may generate the web attribution airingreport. The source of the airing may be listed, along with an airing ID,a station name, a media market, an air date, and a program name.

FIG. 19 depicts a graphical representation of a step involved inselecting a web spike attribution report in a graphical user interface.As shown in FIG. 19, a user may elect to export the generated the webattribution airing report to various file types. The source of theairing may be listed, along with an airing ID, a station name, a mediamarket, an air date, and a program name.

An example command-line report may be as follows:

—Monitorplus

exec Report.WebAttributionAiringOutput@WebAttributionID=N′2013′,@StartDate=′2014-09-3000:00:00′,@EndDate=′2015-04-27 00:00:00′,@CPAGoal=50

Web Spike Media Performance Report

Table 5 shown below depicts a web spike media performance report. Theweb spike media performance report, as provided in the table below,shows the web delta response per million impressions for televisionadvertisements for a video game product that appeals to young males. Thereport may be aggregated to Station-Programs for reporting to indicatethat G4-XPLAY and COM-FUTURAMA produced high web responses perimpression in response to the TV ad. A web media performance report mayinclude one or more of the following parameters measureid, delta webresponse, impressions, timpressions, tratio, airings, delta web responseper impression, programname, and/or dimension to report. Dimension toreport may be a number of categorical variables pertaining to the mediaincluding: (a) Creative (b) ProgramName, (c) DayPart, (d) Network, (e)Station-Program, (f) HourOfDay, and so on.

TABLE 5 delta per Airing p < Row Labels imp tratio count deltaImpressions 0.05 ESPNU-WCC 838.2016764 0.440471627 1 33 39370  4%TOURNAMENT G4-X-PLAY 292.2793384 0.328134431 2 13 131119 24%ESQR-MILLION 320.0682812 0.120060944 1 18 56238 16% DOLLAR LISTING NYFS1-UFC TONIGHT 314.7681825 0.519125566 1 51 162024  0% ESQR-ESQ MOVIE314.4230337 0.163118645 1 21 66789 12% ENN-MIKE & MIKE 106.93662810.422087641 2 4 115066 41% IN THE MORN COM-FUTURAMA 202.30906650.521922461 1 59 291633  0% ESPNU-NIT 194.4768573 0.122710335 1 3 1542643% TOURNAMENT ESP2-NBA 192.0629967 0.534514717 1 16 83306 19% REGULARSEASON REPEAT FS1-FOX SPORTS 61.4362091 0.445594155 11 2.454545455610867 45% LIVE NFLN-NFL AM 168.7915364 0.445831511 2 9 115679 31%

FIG. 20 depicts a graphical representation of steps involved ingenerating a web media performance report. As shown in FIG. 20, a usermay select an advertiser, select a measure and parameter setting, selecta dimension to report on, and view the report.

Tables 6, 7, 8, and 9, as shown below, show examples of a web mediaperformance report with dimensions Creative, Program, Network, andDaypart.

TABLE 6 Performance by Creative Media Cost per Asset Sum Sum Sum WPMCost Per Delta Pattern of of of Sum of Sum of Lower Delta estimated(copy) WPM airings Delta Cost ImpressionsBest Impressions 95 EstimatedLower 95 Male - 33.39 220 934 95,674 27,986,937 27,855,715 28.97 102.39117.98 Remember Thy Name: 30 Original - 32.06 129 478 43,252 14,921,59913,974,512 26.52 90.42 109.28 Male& Female - Remember Thy Name: 30Female - 28.98 192 793 89,069 27,371,567 27,695,223 24.88 112.30 130.80Remember Thy Name: 30 Female - 26.26 133 722 92,585 27,483,42428,894,854 21.80 128.27 154.54 Divorce: 15 NULL 43.04 42 137 2,0843,175,603 1,323,532 30.02 15.25 21.86 Original - 20.62 75 201 31,9329,769,915 11,546,004 15.95 158.50 204.87 Female - Divorce: 15 Male -14.71 143 288 66,682 19,556,226 21,719,899 12.30 231.86 277.32 Car: 15Original - 13.98 26 29 3,339 2,041,528 1,672,426 8.60 117.02 190.08Male - Car: 15

TABLE 7 Performance by DayPart Media Cost per Asset WPM Cost Per DeltaPattern Sum of Sum of Lower Delta estimated Sum of Sum of (copy) WPMairings Delta 95 Estimated Lower 95 Cost ImpressionsBest Weekday- 31.82201 1,614 27.42 145.76 169.14 235,195 50,713,095 5-LateFringe Weekday-11.92 272 525 9.95 224.48 254.75 117,950 46,522,066 6-Overnight Weekday-12.69 273 360 11.19 265.61 301.35 95,609 28,363,300 1-Morning Weekday-31.20 558 2,772 28.61 115.09 125.51 319,022 88,832,808 2-DaytimeWeekday- 36.57 245 1,898 31.99 102.18 116.81 193,923 51,898,4033-EarlyFringe Weekday- 31.95 241 4,339 27.92 153.51 175.69 666,060135,806,710 4-Prime Weekend- 30.48 91 813 24.22 153.80 193.57 125,01326,668,497 5-LateFringe Weekend- 11.92 122 290 9.80 199.42 242.44 57,87124,351,323 6-Overnight Weekend- 13.45 110 158 10.94 305.09 375.21 48,28411,762,487 1-Morning Weekend- 38.43 186 1,910 32.90 115.95 135.41221,443 49,699,898 2-Daytime Weekend- 43.61 170 2,319 37.06 101.47119.42 235,280 53,162,555 3-EarlyFringe Weekend- 34.81 99 1,764 27.95130.05 161.95 229,434 50,685,407 4-Prime

TABLE 8 Performance by Network Media Cost per Asset WPM Cost Per DeltaPattern Sum of Sum of Lower Delta estimated Sum of Sum of (copy) WPMairings Delta 95 Estimated Lower 95 Cost ImpressionsBest SPK 54.61 1201,902 44.84 57.69 70.26 109,732 34,830,867 A&E 53.42 10 180 20.31 65.70172.81 11,847 3,375,555 TRU 51.28 215 1,680 44.43 94.87 109.51 159,41832,768,287 FX 46.93 95 1,023 37.49 123.51 154.60 126,335 21,797,404 COM44.37 98 1,105 35.58 125.63 156.64 138,825 24,906,028 VH1 43.92 1961,146 37.77 70.99 82.54 81,335 26,089,713 LIFE 36.79 164 1,520 31.16103.18 121.82 156,835 41,318,306 MTV 33.67 118 1,037 27.59 226.66 276.56235,148 30,813,823 AMC 33.57 171 2,457 28.54 101.90 119.86 250,36473,180,640 BET 32.31 20 160 18.15 135.00 240.33 21,585 4,947,878 TBS31.47 53 1,331 22.99 244.25 334.23 325,058 42,294,566 TLC 30.91 17 18916.22 168.88 321.91 31,993 6,128,819 CMT 30.32 225 1,142 26.36 76.2187.67 87,057 37,668,946

TABLE 9 Performance by Program Media Cost per Asset WPM Cost Per DeltaPattern Sum of Sum of Lower Delta estimated Sum of Sum of (copy) WPMairings Delta 95 Estimated Lower 95 Cost ImpressionsBest NBATV-OPEN706.37 1 70 — 10.97 99,999,999.00 765 98,707 COURT CENT-SINGLE 422.53 214 — 9.71 99,999,999.00 138 33,569 LADIES (SERIES) CENT-COSBY 386.64 240 — 7.94 99,999,999.00 319 103,811 SHOW VH1C-GUNS N 246.69 1 8 — 16.4799,999,999.00 126 31,032 ROSES LIVE FROM O2 VH1C-BON 200.99 1 2 — 21.6999,999,999.00 33 7,463 JOVI IN CONCERT VH1C- 160.31 2 7 — 26.1699,999,999.00 173 41,268 ENTOURAGE FUSE- 154.83 1 4 — 14.1699,999,999.00 61 27,616 CHRONICLES, THE ESP2- 142.58 1 10 — 145.4399,999,999.00 1,395 67,267 COLLEGE BASKETBALL LIVE TRU-STORAGE 109.55 114 — 21.80 99,999,999.00 311 130,07 HUNTERS VH1C- 109.18 1 1 — 26.5199,999,999.00 22 7,713 DOWNLOAD FESTIVAL 2014 FUSE-TOP 100 99.25 1 3 —25.77 99,999,999.00 84 32,839 HOUSE PARTY SONGS MTV-FRESH 99.17 1 14 —60.05 99,999,999.00 863 145,001 PRINCE OF BEL-AIR CENT-CSI:NY 91.29 3 5— 14.10 99,999,999.00 65 50,699 TRU-SOUTH 90.83 6 67 18.15 32.62 163.242,173 733,451 BEACH TOW VH1-BLACK 90.02 12 233 39.08 27.89 64.23 6,5112,593,470 INK CREW 3 TRU-BAIT CAR 89.47 1 9 — 33.65 99,999,999.00 29196,738

FIG. 21 depicts a screenshot of a web spike media performance reportusing program as a dimension. The impression actual may be impressionsas reported by Nielson, Rentrak, and/or another provider. Theimpressions actual and impressions best may take time to becomeavailable, and thus, an impressions prediction may be used.

FIG. 22 depicts a screenshot of a web spike media performance reportusing daypart as a dimension. FIG. 23 depicts a screenshot of a webspike media performance report using hour as a dimension. FIG. 24depicts a screenshot of a web spike media performance report usingnetwork as a dimension. FIG. 25 depicts a screenshot of a web spikemedia performance report using program as a dimension. FIG. 26 depicts ascreenshot of a web spike media performance report using creative as adimension.

Web Spike Halo Report

A web spike halo report may show a “halo” of the ad airing and an adairing's impact across multiple measures. For each measure and for agiven unit change in targeting, such as how many additional web visits,sessions, searches, and so on, a halo report may be generated. A haloreport may include one or both of the following parameters measureid,slope, percent change, and/or RMSE.

Slope may be calculated by parameter β₁, and may be a measure of thechange in traffic per million impressions assuming a tratio of 1. Thisestimate may take into account the targeting and impression weight ofthe traffic and normalizes this out.

Percent Change may be calculated as discussed above, and may be ameasure of the percentage change in traffic observed when televisionairing ran. The Percent Change measure may not normalized for number ofimpressions, but could be normalized if desired.

RMSE may be the root mean squared error for the fit that was used tocalculate the slope, and may give a measure of the variability of thelandscape. If the RMSE is too high, then the slope estimate may beconsidered unreliable or spurious. Other metrics may be used to measurefit quality including R square and so on.

FIG. 27 depicts an example of a web spike halo report. As shown in FIG.27, the web spike halo report analysis of web activity changes given atelevision commercial. Different classes of web traffic are shown withthe average change in web activity. FIG. 27 shows that email signupsincrease the most within 1 minute of a TV airing (90%), followed bymobile and tablet visits (75%), cost per click or paid search CPCtraffic (71%), and new users (61%).

FIG. 28 depicts an example of a web spike halo report. As shown in FIG.28, the web spike halo report analysis of web activity changes given atelevision commercial. The information provided in FIG. 28 is agraphical representation of the information provided in Table 2, asshown above. The graph is sorted in order of highest web response tolowest. This shows that new homepage mobile traffic produces the highestincrease in traffic during a television airing, with a 76% change. Totalvisits increases by 10.2%. The least affected categories include email,display, and product page traffic (deep-linked pages on specificproducts that a user would have to navigate to).

Table 2, as shown above, also depicts an example of the halo report intable form. For example, for every 1 million impressions at tratio=1,there will be 29.7 additional visits, 28.6 additional unique visitors,24.3 new (i.e. non-returning) visitors, 18 new homepage visits, 8 mobilevisits, and 3.9 new mobile homepage visits, and 5 Search EngineMarketing referrals. Note that in terms of spike magnitude, mobiletraffic may have the highest spike magnitude (around 34% in the previousfigure), whereas in absolute terms there may not be very much mobiletraffic, and so this only translates into 8 mobile visitors. Incontrast, Visits had a very low spike magnitude, e.g., 10.2%, buttranslates into a high absolute amount of traffic (29.7 additionalvisits).

FIG. 29 depicts an example of a web spike halo report. As shown in FIG.29, the web spike halo report is translated from FIG. 28. Thepercentages are translated into total traffic and shows that while themobile traffic had the highest spike in FIG. 28, in absolute terms,visits category increased the most. In FIG. 29, for every 1 milliontelevision media impressions, total traffic generated across a widerange of traffic classes. For every 1 million impressions at tratio=1,there may be 29.7 additional visits, 28.6 additional unique visitors,24.3 new (i.e., non-returning) visitors, 18 new homepage visits, 8mobile visits, 3.9 new mobile homepage visits, and 5 search enginemarketing referrals.

Web Spike Cluster Responsiveness Report

The same algorithm for measuring a web spike halo effect may also beused to measure the “responsiveness” of different target clusters. Acluster may be a segment of the customer population in which anadvertiser may want to reach through advertising. Target clusters may bea set of persons who share certain traits. For example, targets may havea similar age, gender, income, number of children, and so on.

The quality of targeting against each segment may be first defined by atratio score against each target. For example, if there are threeclusters, then there would be three tratios reported for each airing:tratio1, tratio2, tratio3. It may then be possible to do the slopeanalysis on each target, slope1, slope2, slope3. These slopes mayprovide an estimate of how responsive each of the customer targets areto the advertiser's impressions.

Web spike cluster responsiveness reports and halo reports may includeone or more of SourceKey, Slope, PercentChange, and/or RMSE.

FIG. 30 depicts an example of a web spike cluster response analysis. Asshown in FIG. 30, population segments may generate the best web responseafter targeting them with a television ad. This shows that for aparticular advertiser, younger females may be far more responsive totheir offer than middle-aged females. High income males may also beresponsive, but not as high as the younger females. Using thisinformation, the advertiser may focus their targeting on the youngerfemales.

Although the web spike cluster response analysis may be similar to theweb spike halo analysis, the ability to focus targeting may be extremelyimportant in advertising. An advertiser may significantly decrease theirmedia budget, or may move more of their marketing dollars onto segmentsthat are responsive and interested in their offer. The above analysiscan also normalize against baseline responsiveness for differentsegments

Automatically Optimize Parameters

A variety of parameters may be associated with a web attributionincluding one or more of a measure, a filter, a lower and/or upperexclusion window, a maximum number of airings, a maximum impression, atime grain, a full attribution, a partial attribution, and/or ageographic grain.

Measure parameters may include one or more web measure being used, suchas visits, sessions, and/or unique visitors. Filter parameters mayinclude a choice of filters, such as new visitors, direct-to-sitereferrals, null referrers, and/or mobile or tablet user agents. A lowerand/or upper exclusion window parameter may include a time prior to anairing and/or after an airing that should be free of additional mediaairings and/or should meet the maximum thresholds below. A maximumnumber of airings parameter may include an exclusion period where thereshould be no more than this number of airings. A maximum impressionparameter may include an exclusion period where there should be no morethan this number of impressions from other media events. A time grainparameter may include 1 minute, 2 minutes, 5 minutes, 15 minutes, etc.,which may be used to bucket media and web events during attribution.

A full attribution and/or a partial attribution parameter may allowsimultaneous airings. If a full attribution parameter is used,simultaneous airings occurring together within a bucket may beattributed by apportioning credit based on their impressions or othercriteria. If a partial attribution parameter is used, then if there aremultiple simultaneous airings, the airings are not attributed.

If a geographic grain parameter is used, then airings and web activitymay be aggregated to the national level. If DMA geographic grainparameter is used, then local airings may be matched with local webactivity. Local web activity may be measured by performing an IP-lookupon the web traffic to classify them into different geographic areas.

Each of the above mentioned parameters may be automatically optimized.Automatic optimization may be performed through one or more of thefollowing steps. First, several combinations of the above mentionedparameters may be defined. Then, each of the defined parameter settingsmay be executed and attribution results may be generated. Next, anaverage spike percentage may be calculated, and then, targetinglandscape parameters may be calculated (e.g., slope and intercept). Thesum of squared error of the landscape may then be calculated, and apercent of cases attributed may be calculated. After executing allcombinations, the parameter settings that have the highest percentageweb spike or lowest root mean squared error, may be selected as theoptimal settings. It also may be possible for a user to set parameters,and the user may use a lower spike magnitude parameter setting if itattributes more of the airings.

Tables 10 and 11 depict parameter search results. As shown in tables 10and 11, slope, intercept, and spikepctaverage diagnostics may be visibleand may show which of the metrics and parameter settings are best.

TABLE 10 Automated Parameter Search Results Aggregate DataWebAttribution By Time ID UseForScoring SourceKey MeasureID MarketWindow 1186 0 110401 27 0 5 1187 0 110401 36 0 5 1188 0 110401 37 0 51189 0 110401 38 0 5 1190 1 110401 39 0 5 1191 0 110401 40 0 5 1192 0110401 41 0 5 1193 0 110401 42 0 5 1194 0 110401 43 0 5 1195 0 110401 440 5 1196 0 110401 45 0 5 1197 0 110401 46 0 5 1198 0 110401 47 0 5 11990 110401 48 0 5 1200 0 110401 49 0 5 1201 0 110401 50 0 5 1202 0 11040151 0 5 1203 0 110401 52 0 5 1204 0 110401 53 0 5 Lower Upper Media MediaExclusion Exclusion WebAttribution Time Time Full Minimum Minimum SpikeID Window Window Attribution Impressions Airings PctAvg 1186 5 0 1 0 18.13 1187 5 0 1 0 1 5.33 1188 5 0 1 0 1 5.71 1189 5 0 1 0 1 1.23 1190 50 1 0 1 49.56 1191 5 0 1 0 1 20.75 1192 5 0 1 0 1 18.62 1193 5 0 1 0 16.04 1194 5 0 1 0 1 3.38 1195 5 0 1 0 1 1.43 1196 5 0 1 0 1 0.38 1197 50 1 0 1 −0.97 1198 5 0 1 0 1 14.71 1199 5 0 1 0 1 14.71 1200 5 0 1 0 118.62 1201 5 0 1 0 1 0.97 1202 5 0 1 0 1 23.1 1203 5 0 1 0 1 15.52 12045 0 1 0 1 4.02 WebAttribution Intercept Attributed ID Slope InterceptSlopePositive Positive Pct 1186 71.5267392527873 39.038 20.018977114576471.1097526769653 100 1187 73.4298947723419 44.88 6.2941605530594186.9884991806818 100 1188 74.5321986620914 43.611 15.241204217118580.0973288674321 100 1189 2.11156132415068 4.653 −23.742021520089524.5104945123655 100 1190 21.6379045353731 10.76 11.415872848678817.3626216180327 100 1191 48.9899407382675 25.226886765378324.3027875833938 42.3659602587061 100 1192 63.172892030126936.2085194033426 31.7220337694906 58.5159669009066 100 119314.8459978589929 7.93651452264115 −7.92096481836447 22.6648277367109 1001194 8.20463920440668 3.4944112499625 −11.7635507401761 16.1815419587978100 1195 0.370726975440122 −0.0536844232695239 −1.268770723926821.44485559915304 100 1196 −0.216353179267301 0.461628775126748−6.03572589886188 5.89637327881743 100 1197 1.073521828265310.150666255260131 −0.713183104635479 3.22129599897471 100 119850.9786592731211 26.1006194510393 22.9684907708998 45.753036964897 1001199 50.9786592731211 26.1006194510393 22.9684907708998 45.753036964897100 1200 63.9104089917169 36.1424242000119 32.401576781565158.4550520746715 100 1201 12.4729246340856 0.604601187270315−16.2560297904869 18.3561290898712 100 1202 29.702340663530216.182959711954 10.1824920954828 28.7112075726408 100 120321.7606448925032 6.72759549329199 4.42194577731784 16.1540610591827 1001204 20.5219229407963 16.09824709679 −8.81328899012128 39.2906724829881100

TABLE 11 Automated Parameter Search Results measureid measuredisplayspikepctavg slope intercept 39 New Home 69.49 −9.89 41.98 Mobile 39NewHome 63.29 39.5253909181814 38.9308545522491 Mobile 39 New Home 59.7457.0711675993503 34.7596662541643 Mobile 39 New Home 49.5621.6379045353731 10.7670927353409 Mobile 39 New Home 48.6656.5067175325521 25.3215247590866 Mobile 51 Mobile 31.9621.0013824830447 56.7687143649657 51 Mobile 30.53 65.186877813576458.181085079446 40 New Home 29.73 −54.2311405559028 99.8869087604818Direct 51 Mobile 28.84 63.9242894228157 55.3681916546479 40 New Home28.14 103.601599213457 83.5833123492095 Direct last run measureidslopepositive interceptpositive webattributionid date 39 −25.991 47.3751385 2014 May 14 22:43:05 39 26.71 44.53 1271 2014 May 15 12:27:05 3939.1374571066732 41.16 1252 2014 May 15 12:13:42 39 11.415872848678817.36 1190 2014 May 15 11:12:39 39 46.3683420290059 30.50 1209 2014 May15 11:32:02 51 −68.7648680827587 82.1378259402728 1397 2014 May 1422:48:06 51 −3.95160037991875 79.4309147254221 1283 2014 May 15 12:32:4740 −134.06907897743 129.252838808494 1386 2014 May 14 22:43:18 5111.9340027195814 73.9906286662082 1264 2014 May 15 12:20:19 4038.5908166115748 111.970318305361 1272 2014 May 15 12:27:21 measureiduseforscoring aggregatebymarket datatimewindowlowermediaexclusiontimewindow 39 0 0 5 120 39 0 0 5 60 39 0 0 5 30 39 10 5 5 39 0 0 5 5 51 0 0 5 120 51 0 0 5 60 40 0 0 5 120 51 0 0 5 30 40 00 5 60 measureid uppermediaexclusiontimewindow fullattributionminimumimpressions minimumairings 39 0 0 0 1 39 0 0 0 1 39 0 0 0 1 39 01 0 1 39 0 0 0 1 51 0 0 0 1 51 0 0 0 1 40 0 0 0 1 51 0 0 0 1 40 0 0 0 1

Automated Ad Targeting Feedback Loop

Web spikes may be used to measure which networks, programs,times-of-day, and creative generate the highest response. Rather thanjust reporting this data, the data may be used to automatically optimizea television campaign to maximize the web site response; using a “closedfeedback loop” of web spike data to automatically adjust TV targeting.

An example of an ad targeting problem may be to select media in order ofvalue per dollar, using the following formula:

$M_{i}:\max\frac{{wpi}\left( M_{i} \right)}{{CPI}\left( M_{i} \right)}$

Where CPI (M_(i)) and wpi(M_(i)) are clearing prices and mediaobservations. CPI (M_(i)) may be obtained from the TV stations. The webspike of an upcoming airing wpi(M_(i)) may be estimated. In order toestimate the performance of the airing, the airing may be broken into aseries of features including station, program, and so on. For example, afuture media instance, M_(i) may be (CNN, 8 pm, “Piers Morgan,” Tuesday,Dec. 12, 2012, Pod1, Pos2, 60 s). The following features may be used topredict the performance: Station wpi(m_(i1))=(CNN), Station-Hour-Podm_(i2)=(CNN, 8 pm, Pod1), Geography-Station m_(i3)=(National-CNN), andso on.

Table 12 depicts networks with highest web response per impressionmeasured in a TV campaign.

TABLE 12 Network-Day-Hour WPI DFH 0.00231 COM 0.00217 SOAP 0.00121

Tables 13 and 14 depict examples of aggregated features forStation-day-hour and Station-Program. Table 13 depicts Network-Day-Hourswith highest web response per impression measured in the TV campaign.

TABLE 13 Network-Day-Hour WPI SOAP - Su - 3 pm 0.00322 COM - Tu - 1 pm0.00302 DFH - Tu - 11 am 0.00289 DFH - W - 2 pm 0.00273 DFH - M - 7 am0.00259 COM - W - 1 pm 0.00253 DFH - M - 1 pm 0.00229 COM - Th - 1 pm0.00214 COM - Tu - 12 pm 0.00211 DFH - Th - 3 pm 0.00206

Table 14 depicts Network Programs with highest web response perimpression in the TV campaign.

TABLE 14 Network-Program WPI LMN - Movie 0.0203 SOAP - Veronica Mars0.0147 SOAP - One Tree Hill 0.0107 AMC - AMC Movie 0.0085 SOAP - GilmoreGirls 0.0076 OWN - Dr. Phil 0.0073 SOAP - General Hospital 0.0067 WGNA -Law & Order: Criminal Intent 0.0065 SOAP - Beverly Hills, 90210 0.0054COM - South Park 0.0046

For each feature, an estimate may be created, which is equal to theaverage web response per impression over all historical media, asfollows:

${{wpi}(m)} = {{\frac{\sum_{i}{{{wpi}\left( M_{i} \right)} \cdot {I\left( M_{i} \right)}}}{\sum_{i}{\cdot {I\left( M_{i} \right)}}}:m} \in M_{i}}$

The prediction of performance for an upcoming media M_(i) then becomesthe weighted average of these historical estimates of wpi. The weightsmay be trained to predict future wpi.

${{wpi}\left( M_{i} \right)}^{*} = {{\sum\limits_{j}{{\frac{w_{j}}{\sum_{i}w_{i}} \cdot {{wpi}\left( m_{j} \right)}}:m}} \in M_{i}}$

For example, to score “SOAP—Beverly Hills, 90210,” which has moved to anew time of Sunday at 4 pm. Historical readings may be checked, and SOAPmay have an average WPI of 0.00121, and previous airings ofSOAP-Beverley Hills, 90210 may produce WPI of 0.0054. The performance ofSOAP-Sun-4 pm may be missing, as information for SOAP-Sun-3 pm does notmatch, and thus, may not be used. Pre-calculated weights of 0.25, 0.5,and 0.25 for Network, Station-Program, and Station-Day-Hour features maybe calculated. Therefore, the prediction for WPI if purchasing anupcoming airing would be(0.25/0.75)*0.00121+(0.5/0.75)*0.0054+NULL=0.0040.

As new airings occur, and web spikes occur, wpi statistics may beupdated for media by aggregating in the latest web spike responseinformation, which is an estimate of wpi(M_(i)) for upcoming buyablemedia.

As new airings occur, and web spikes occur, wpi statistics may beupdated for media per above. Then, the media bought may be changed byre-calculating a ranking function, as follows:

$M_{i}:\max\frac{{wpi}\left( M_{i} \right)}{{CPI}\left( M_{i} \right)}$

This may reveal that the best media to buy might be SOAP—Sa—3 pm basedon knowledge of the program that will be on at that time, the webresponse for that daypart and network. This results in a system whichreceives new web spike data, and optimizes what is being purchased inthat campaign.

Example Embodiment

The attribution algorithms were run on a TV campaign and associated webactivity from Feb. 11, 2013 to July 2013. The television campaigncomprised 35,296 airings. Both attribution methods were calculated, andthen compared to the web response per impression calculated using eachalgorithm to the targeting score of each airing as calculated using analgorithm. The comparison measures the number of buyers per million inthe viewing audience. The Instantaneous model has a fit of R=0.64,day-hour subtraction with 3 hour exclusion has a fit of R=0.42, andday-hour subtraction without exclusion produces a fit of R=0.33.

Reports may be provided on the performance of different networks,day-parts and programs in generating web spikes by dividing TV-generatedweb activity by TV impressions.

${{wpi}(M)} = \frac{{\sum{W\left( {T,G} \right)}} - {W_{NoTV}\left( {T,G} \right)}}{I\left( {M\left( {T,G} \right)} \right)}$

The highest web-spike per impression network-programs andnetwork-day-parts for the live TV campaign, as measured by the firstalgorithm as described above, are shown in Tables 13 and 14 above. Themost responsive networks were Discovery Health and Fitness, SOAP andComedy. The most responsive programs were “Veronica Mars,” “One TreeHill” and “Gilmore Girls.” The product being advertised was one thatappealed to higher income women who were just married or wererenovating. Table 13 shows a network day-hours with a highest webresponse per impression measured in the TV campaign. Table 14 showsnetwork programs with a highest web response per impression in the TVcampaign.

The web response alignment graphs are suggestive. However, TV is knownto have complex and long ranging effects. Thus, the question is whetherweb spike response could be used as a proxy to measure total TV effect?

The total TV lift should be measured. A classic method for measuringtotal TV effect is to run a controlled experiment. Media is applied tocertain geographies, and not to control geographies. The difference inweb activity between treatment and control is then measured. This iscalled a Matched Market Experiment, and it has been used in manyprevious studies to measure television effects.

In evaluation, a Matched Market Experiment was implemented by purchasing9,748,347 impressions and 296 airings ($483 per airing) of media on theweek of Feb. 11, 2014 and Mar. 4, 2013 in treatment marketG_(j)=Seattle. This purchased approximately 281 Gross Rating Points perweek in the targeted area. A Gross Rating Point (“GRP”) is equal to100*impressions per TV Household per area per unit time. For example,281 GRPs per week is equal to 2.81 impressions per household per week.

An aggregated control W(T,G_(j,CON)) was selected and matched to thistreatment and not running media as follows. The control areas wereactually subjected to approximately 20 GRPs of advertising weight due tosome national advertising that was unavoidable, so the comparison was281 GRPs in treatment versus 20 GRPs in control.

${W\left( {T,G_{j,{CON}}} \right)} = {{{\sum\limits_{i \neq j}{w_{i} \cdot {W\left( {T,G_{i}} \right)}}} + {w_{0}:{\sum{I\left( {M\left( {T,G_{i}} \right)} \right)}}}} = 0}$

Where w_(i) were trained using data from times T₀ that were prior to thestart of television, selected using stepwise regression to avoidover-fitting, and the model was validated against a test set that washeld out in time. The parameters are shown in Table 16 below.

$\min w_{i}:{\sum\limits_{T \in {TRAIN}}\left( {{W\left( {T_{0},G_{j}} \right)} - {\sum\limits_{i}{w_{i} \cdot {W\left( {T_{0},G_{i}} \right)}}} + w_{0}} \right)^{2}}$

Difference of differences can now be used to calculate the activity dueto the treatment in this kind of design. The method measures the changein treatment area minus change in control area:

ƒ(M(T,G _(j))))=(W(T,G _(j))−W(T ₀ ,G _(j)))−(W(T,G _(j,CON))−W(T ₀ ,G_(j,CON)))

Because an explicit, time-varying control which minimizes differencebetween W(T₀,G_(j,CON)) and W(T₀, G_(j)) is used, the treatment andcontrol starting terms cancel, and the difference of difference formulabecomes the formula below. The results are shown in Table 15 and FIGS.31a-31f below.

f(I(M(T, G_(j)))) = W(T, G_(j)) − W(T, G_(j, CON))${lift} = {\frac{W\left( {T,G_{j}} \right)}{W\left( {T,G_{j,{CON}}} \right)} - 1}$

The result shows that web spike lift readings appear to predict total TVeffect. Web Spike analysis reported 30.7% lift for Homepage, 14.8% fornew visitors, and 10.2% for visits. Experimental measurement exhibitsthe same relationship: 58%, 27%, and 18% (see Table 15; FIGS. 31A-31F).

Another result is that the amount of lift measurable by web spike issmall relative to the total effect of TV. Experiments providedmeasurements of an additional 3.5 conversions for every conversiongenerated during the campaign in the 6 months after the campaign becauseof elevated lift in treatment area. Web spike may be unable to detectthis lift as it works on short-term effects. In addition, web spikemeasurements only observe a narrow time window around each airing whensignal-to-noise is maximum. Based on the first algorithm, 0.69% of totalweb effect including residuals after 6 months were measure.Nevertheless, despite measuring only a small amount of TV's totaleffect, the measured signal appears to be correlated with overall TVeffect.

TABLE 15 Experimental Lift versus Web Spike Lift Exp Corr Exp web spike% metric, web spike Traffic % lift lift/base conversions timp corr.Homepage 57.8% 30.7% 0.321 0.079 New visitors 27.4% 14.8% 0.162 0.064Visits 18.3% 10.2% 0.130 0.054

Robust measurement of web-spikes may be performed, and the informationgather may be used to automatically optimize a television campaign.

FIGS. 31A and 31B show visits. FIG. 31A shows a web spike percentagelift for 60 minutes before through 60 minutes after a TV airing (atminute 0) with spike magnitude expressed in terms of percentage liftover average baseline activity. FIG. 31B shows an experimental liftmeasurement measured using difference of differences calculated over atreatment and control geographic area for three web metrics. The timeseries are shown as a 7 day moving average. The control area (lightershaded line) with web activity is normalized to 1.0, and treatment area(darker shaded line) shows activity in units of percentage over control.As web spike percentage lift increases, the experimentally measured liftincreases.

FIGS. 31C and 31D show new visitors. FIG. 31C shows a web spikepercentage lift for 60 minutes before through 60 minutes after a TVairing (at minute 0) with spike magnitude expressed in terms ofpercentage lift over average baseline activity. FIG. 31D shows anexperimental lift measurement measured using difference of differencescalculated over a treatment and control geographic area for three webmetrics. The time series are shown as a 7 day moving average. Thecontrol area (lighter shaded line) with web activity is normalized to1.0, and treatment area (darker shaded line) shows activity in units ofpercentage over control. As web spike percentage lift increases, theexperimentally measured lift increases.

FIGS. 31E and 31F show new visitors to a homepage from mobile devices.FIG. 31E shows a web spike percentage lift for 60 minutes before through60 minutes after a TV airing (at minute 0) with spike magnitudeexpressed in terms of percentage lift over average baseline activity.FIG. 31F shows an experimental lift measurement measured usingdifference of differences calculated over a treatment and controlgeographic area for three web metrics. The time series are shown as a 7day moving average. The control area (upper, lighter shaded line) withweb activity is normalized to 1.0, and treatment area (lower, darkershaded line) shows activity in units of percentage over control. As webspike percentage lift increases, the experimentally measured liftincreases.

FIG. 32 is a simplified functional block diagram of a computer that maybe configured as a client, agent, or server for executing the methoddescribed above, according to exemplary an embodiment of the presentdisclosure. Specifically, in one embodiment, as shown in FIG. 32, any ofservers and/or systems implementing the above-described disclosure maybe an assembly of hardware 3200 including, for example, a datacommunication interface 3260 for packet data communication. The platformmay also include a central processing unit (“CPU”) 3220, in the form ofone or more processors, for executing program instructions. The platformtypically includes an internal communication bus 3210, program storage,and data storage for various data files to be processed and/orcommunicated by the platform such as ROM 3230 and RAM 3240, although thesystem 3200 often receives programming and data via networkcommunications 3270. The server 3200 also may include input and outputports 3250 to connect with input and output devices such as keyboards,mice, touchscreens, monitors, displays, etc. Of course, the variousserver functions may be implemented in a distributed fashion on a numberof similar platforms, to distribute the processing load. Alternatively,the servers may be implemented by appropriate programming of onecomputer hardware platform.

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine-readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links, or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

While the presently disclosed sharing application, methods, devices, andsystems are described with exemplary reference to mobile applicationsand to transmitting HTTP data, it should be appreciated that thepresently disclosed embodiments may be applicable to any environment,such as a desktop or laptop computer, an automobile entertainmentsystem, a home entertainment system, etc. Also, the presently disclosedembodiments may be applicable to any type of Internet protocol that isequivalent or successor to HTTP.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-20. (canceled)
 21. A computer-implemented method for web spikeattribution, the method comprising: filtering, by a server, websiterequest data to include at least one of null referrers,previously-uncookied visitors, and traffic not having a search queryreferrer; attributing, by the server, a change in the filtered websiterequest data to a media event based on a calculated probability that thechange in the filtered website request data is due to the media event;and generating and displaying, by the server, a user interface elementthat represents attribution of the filtered website request data to themedia event, and that indicates a number of website impressions orrequests associated with the media event.
 22. The method of claim 21,further comprising: determining, by the server, a number of impressionsdue to the media event based on the change in the filtered websiterequest data; and generating, by the server, a report on the change inthe filtered website request data and the number of impressions.
 23. Themethod of claim 22, further comprising: determining, by the server, achange in the filtered website request data per impressions performancebased on media data for a plurality of media events including the mediaevent, the media data including one or more of a network, a day, anhour, a program, and a geography for each media event; and generating,by the server, a report on the change in the filtered website requestdata per impressions performance based on at least one of the network,the day, the hour, the program, and the geography.
 24. The method ofclaim 21, further comprising: filtering, by the server, the filteredwebsite request data to increase signal-to-noise based on anIP-geographic lookup to establish a geographic origin of website requesttraffic.
 25. The method of claim 21, further comprising: determining, bythe server, a number of impressions due to the media event based on thefiltered website request data for a time period during the media eventand the filtered website request data for a time period prior to themedia event; determining, by the server, a change in the filteredwebsite request data per impressions performance based on media data fora plurality of media events including the media event, the media dataincluding one or more of a network, a day, an hour, a program, and ageography for each media event; and determining, by the server, atargeting score of the media event based on the number of impressionsand the change in the filtered website request data per impressions. 26.The method of claim 25, further comprising: generating, by the server, apurchase recommendation based on the targeting score of the media event.27. The method of claim 21, further comprising: determining, by theserver, a number of impressions due to the media event based on thefiltered website request data for a time period during the media eventand the filtered website request data for a time period prior to themedia event; and determining, by the server, a change in the filteredwebsite request data per impressions performance based on media data fora plurality of media events including the media event, the media dataincluding a plurality of a network, a day, an hour, a program, and ageography for each media vent.
 28. A system for web spike attribution,the system comprising: a data storage device that stores instructionsfor web spike attribution; and a processor configured to execute theinstructions to perform a method including: filtering website requestdata to include at least one of null referrers, previously-uncookiedvisitors, and traffic not having a search query referrer; attributing achange in the filtered website request data to a media event based on acalculated probability that the change in the filtered website requestdata is due to the media event; and generating and displaying a userinterface element that represents attribution of the filtered websiterequest data to the media event, and that indicates a number of websiteimpressions or requests associated with the media event.
 29. The systemof claim 28, wherein the processor is further configured to execute theinstructions to perform the method including: determining a number ofimpressions due to the media event based on the change in the filteredwebsite request data; and generating a report on the change in thefiltered website request data and the number of impressions.
 30. Thesystem of claim 29, wherein the processor is further configured toexecute the instructions to perform the method including: determining achange in the filtered website request data per impressions performancebased on media data for a plurality of media events including the mediaevent, the media data including one or more of a network, a day, anhour, a program, and a geography for each media event; and generating areport on the change in the filtered website request data perimpressions performance based on at least one of the network, the day,the hour, the program, and the geography.
 31. The system of claim 28,wherein the processor is further configured to execute the instructionsto perform the method including: filtering the change in the filteredwebsite request data to increase signal-to-noise based on anIP-geographic lookup to establish a geographic origin of website requesttraffic.
 32. The system of claim 28, wherein the processor is furtherconfigured to execute the instructions to perform the method including:determining a number of impressions due to the media event based on thechange in the filtered website request data for a time period during themedia event and the change in the filtered website request data for atime period prior to the media event; determining a change in thefiltered website request data per impressions performance based on mediadata for a plurality of media events including the media event, themedia data including one or more of a network, a day, an hour, aprogram, and a geography for each media event; and determining atargeting score of the media event based on the number of impressionsand the change in the filtered website request data per impressions. 33.The system of claim 32, wherein the processor is further configured toexecute the instructions to perform the method including: generating, apurchase recommendation based on the targeting score of the media event.34. The system of claim 28, wherein the processor is further configuredto execute the instructions to perform the method including: determininga number of impressions due to the media event based on the filteredwebsite request data for a time period during the media event and thefiltered website request data for a time period prior to the mediaevent; and determining a filtered website request data per impressionsperformance based on media data for a plurality of media eventsincluding the media event, the media data including a plurality of anetwork, a day, an hour, a program, and a geography for each mediaevent.
 35. A non-transitory machine-readable medium storing instructionsthat, when executed by a computing system, causes the computing systemto perform a method for web spike attribution, the method including:filtering website request data to include at least one of nullreferrers, previously-uncookied visitors, and traffic not having asearch query referrer; attributing a identified change in the filteredwebsite request data to a media event based on a calculated probabilitythat the change in the filtered website request data is due to the mediaevent; and generating and displaying a user interface element thatrepresents attribution of the filtered website request data to the mediaevent, and that indicates a number of website impressions or requestsassociated with the media event.
 36. The non-transitory machine-readablemedium of claim 35, the method further comprising: determining a numberof impressions due to the media event based on the change in thefiltered website request data; and generating a report on the change inthe filtered website request data and the number of impressions.
 37. Thenon-transitory machine-readable medium of claim 35, the method furthercomprising: filtering the filtered website request data to increasesignal-to-noise based on an IP-geographic lookup to establish ageographic origin of website request traffic.
 38. The non-transitorymachine-readable medium of claim 35, the method further comprising:determining a number of impressions due to the media event based on thefiltered website request data for a time period during the media eventand the filtered website request data for a time period prior to themedia event; determining, a change in the filtered website request dataper impressions performance based on media data for a plurality of mediaevents including the media event, the media data including one or moreof a network, a day, an hour, a program, and a geography for each mediaevent; and determining a targeting score of the media event based on thenumber of impressions and the change in the filtered website requestdata per impressions.
 39. The non-transitory machine-readable medium ofclaim 38, the method further comprising: generating a purchaserecommendation based on the targeting score of the media event.
 40. Thenon-transitory machine-readable medium of claim 35, the method furthercomprising: determining a number of impressions due to the media eventbased on the filtered website request data for a time period during themedia event and the filtered website request data for a time periodprior to the media event; and determining a change in the filteredwebsite request data per impressions performance based on media data fora plurality of media events including the media event, the media dataincluding a plurality of a network, a day, an hour, a program, and ageography for each media event.